Smartphone Photogrammetry for Facial Asymmetry Measurement

Article
Evaluating the Accuracy of Smartphone-Based Photogrammetry
and Videogrammetry in Facial Asymmetry Measurement
Luiz Carlos Teixeira Coelho 1,2 , Matheus Ferreira Coelho Pinho 1 , Flávia Martinez de Carvalho 3,4 ,
Ana Luiza Meneguci Moreira Franco 3,4 , Omar C. Quispe-Enriquez 2 , Francisco Airasca Altónaga 1
and José Luis Lerma 2, *
1
2
3
4
*
Academic Editor: Jie Yang
Received: 6 February 2025
Revised: 22 February 2025
Accepted: 26 February 2025
Published: 1 March 2025
Citation: Teixeira Coelho, L.C.;
Pinho, M.F.C.; Martinez de Carvalho,
F.; Meneguci Moreira Franco, A.L.;
Quispe-Enriquez, O.C.; Altónaga, F.A.;
Lerma, J.L. Evaluating the Accuracy of
Smartphone-Based Photogrammetry
Photogrammetry and Remote Sensing Laboratory (Laboratório de Fotogrametria e Sensoriamento
Remoto—LFSR), School of Engineering, Rio de Janeiro State University, Rua São Francisco Xavier 524,
PJLF Sala 4044F, Maracanã, Rio de Janeiro 20550-013, RJ, Brazil; [email protected] (L.C.T.C.);
[email protected] (M.F.C.P.); [email protected] (F.A.A.)
Photogrammetry and Laser Scanner Research Group (GIFLE), Department of Cartographic Engineering,
Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera s/n,
46022 Valencia, Spain; [email protected]
Laboratory of Epidemiology of Congenital Malformations (LEMC—Laboratório de Epidemiologia das
Malformações Congênitas), Instituto Oswaldo Cruz, Avenida Brasil 4365 LEMC, Manguinhos,
Rio de Janeiro 21040-360, RJ, Brazil; [email protected] (F.M.d.C.);
[email protected] (A.L.M.M.F.)
Post-Graduation Programme in Biological Sciences (Genetics), Rua Professor Rodolpho Paulo Rocco, s/n,
Prédio do CCS-Bloco A-2, Andar-Sala 099, Federal University of Rio de Janeiro Ilha do Fundão, Cidade
Universitária, Rio de Janeiro 21941-617, RJ, Brazil
Correspondence: [email protected]
Abstract: Facial asymmetry presents a significant challenge for health practitioners, including physicians, dentists, and physical therapists. Manual measurements often lack the
precision needed for accurate assessments, highlighting the appeal of imaging technologies
like structured light scanners and photogrammetric systems. However, high-end commercial systems remain cost prohibitive, especially for public health services in developing
countries. This study aims to evaluate cell-phone-based photogrammetric methods for
generating 3D facial models to detect facial asymmetries. For this purpose, 15 patients had
their faces scanned with the ACADEMIA 50 3D scanner, as well as with cell phone images
and videos using photogrammetry and videogrammetry, resulting in 3D facial models.
Each 3D model (coming from a 3D scanner, photogrammetry, and videogrammetry) was
half-mirrored to analyze dissimilarities between the two ideally symmetric face sides using Hausdorff distances between the two half-meshes. These distances were statistically
analyzed through various measures and hypothesis tests. The results indicate that, in
most cases, both photogrammetric and videogrammetric approaches are as reliable as 3D
scanning for detecting facial asymmetries. The benefits and limitations of using images,
videos, and 3D scanning are also presented.
and Videogrammetry in Facial
Asymmetry Measurement. Symmetry
2025, 17, 376. https://doi.org/
Keywords: smartphone-based photogrammetry; 3D scanning; cell phone; facial landmarks;
facial anthropometry; dysmorphology
10.3390/sym17030376
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Symmetry 2025, 17, 376
1. Introduction
Measuring facial asymmetries is vital in managing patients with craniofacial anomalies
in clinical practice with applicability in public health [1,2]. A harmonious face, from the
perspective of symmetry, not only influences the perception of beauty but also serves as
an indicator of underlying health [3]. In dysmorphology, the accurate identification of
https://doi.org/10.3390/sym17030376
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craniofacial asymmetries is essential for diagnosing syndromes and deformities, allowing
for early interventions and better therapeutic results [4]. In the context of public health,
understanding the prevalence and impact of these asymmetries helps experts in the field
develop policies and programs aimed at preventing and treating these conditions, which
are often linked to genetic and environmental factors [1]. The early detection and correction
of craniofacial asymmetries, when possible, can significantly improve self-esteem and
quality of life, as it reduces co-morbidities in the medium and long term, highlighting the
importance of these measures for a comprehensive approach to health [5,6].
The analysis of craniofacial asymmetries has various applications in medicine, dentistry, and public health, ranging from the diagnosis of anomalies such as plagiocephaly and
cleft lip to surgical planning and aesthetic rehabilitation with customized prostheses [7]. It
is also used in forensic facial reconstruction for human identification [8]. Facial asymmetry
has a positive correlation with increasing age [9]. The same source also shows that the
middle and lower thirds of the face suffer greater impact. This probably occurs due to a
complex effect of gravity, bone resorption, decreased tissue elasticity, and subcutaneous
fullness [10]. Facial asymmetries may also differ according to gender [11]; in a sample
of young adults (18–25 years) with self-reported European ancestry, we observed greater
variation in male faces than in female faces for all measurements taken.
The state of the art in 3D craniofacial photogrammetric/videogrammetric reconstruction
systems has advanced significantly in recent years, incorporating technologies such as machine
learning and deep neural networks to improve accuracy and efficiency in creating detailed
three-dimensional (3D) models [12–15]. These systems use techniques such as point clouds
and photographic images to generate accurate representations of the skull and face and are
widely used in areas such as medicine, dentistry, and forensic anthropology [16]. However,
the costs involved in implementing these technologies can be high, including the purchase of
specialized equipment, advanced software, and the need for qualified personnel to operate
and interpret the data generated [17]. Despite these challenges, the benefits in terms of
diagnostic accuracy and treatment personalization justify investment in effective approaches
for the identification of head dysmorphologies. For example, 3D craniofacial photogrammetry
can also work as a tool for better phenotyping. For example, in the case of orofacial clefts, it
can help in the identification of subclinical phenotype and assist genetic studies [18].
Recently, smartphone-based photogrammetry has proven to be an effective low-cost
solution for obtaining accurate measurements of patient’s head [19,20]. Photogrammetric
solutions for head measurements may use stickers or marks drawn with a makeup pencil.
They may also include a coded cap, which compresses the patient’s hair, revealing the shape
of the cranium more accurately. Assessing facial asymmetries through photogrammetric
methods, however, presents some challenges concerning the quality of 3D models produced
through photogrammetry. While it has been demonstrated that such solutions can generate
3D models with sufficient accuracy for cranial measurements and validations [19–22], faces
are not entirely static due to involuntary movements. In fact, previous attempts adding
machine learning facial landmarks failed to be as accurate as coded markers, downgrading
the quality of the eventual 3D models [23]. Commercial photogrammetric systems for
capturing facial data typically utilize synchronized arrays of cameras, capturing multiple
images simultaneously [24]. This setup is, however, impractical when adapting the same
algorithms for usage on cell phones shooting with a single camera.
Fully covering a person’s head with coded fabric or stickers is impractical. As a result,
algorithms for accurately measuring faces and generating 3D models from photogrammetry
must leverage natural facial landmarks that provide high-contrast features, making them
suitable for the automatic detection of homologous features. Nevertheless, certain regions
of the face, such as the chin and forehead, may lack sufficient contrast, potentially leading
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to increased errors in photo alignment or orientation. It is also worth noting that every face
exhibits a degree of asymmetry, with only particularly extreme cases being medically classified as pathological [25]. Thus, a certain level of measurement uncertainty can be acceptable
when implementing a cell phone photogrammetric solution for facial dysmorphologies.
Videogrammetry is also considered a potential alternative to single-shot photographs
for two main reasons. First, most cell phones stabilize the focal distance and maintain it
consistently while recording videos [26], ensuring uniformity and guaranteeing a more
robust camera geometry. Second, videogrammetry reduces the need for cumbersome
movements when capturing individual images, which can, otherwise, make it difficult to
produce high-quality overlapping images.
This study aims to explore the potential of using low-cost, cell phone-based photogrammetric techniques to capture, process, and align images of a person’s head, generating a 3D
model that is accurate enough for measuring facial asymmetries. Such 3D models might have
a significant impact on various medical applications. By comparing the 3D models generated
from images, videos, and 3D scanning and applying statistical measures to evaluate their
similarity, this research seeks to determine whether these 3D models can serve as a viable,
cost-effective alternative to expensive 3D imaging medical solutions. Moreover, this study
proposes an innovative and accessible approach based on mobile phone photogrammetry,
eliminating the need for specialized hardware for data acquisition and optimizing the detection of natural facial features to enhance the accuracy of 3D reconstruction, offering a
viable and cost-effective alternative to traditional solutions, which could be used in lieu of
expensive technology, such as 3D scanners or advanced photogrammetric systems.
2. Materials and Methods
The workflow comprised the following tasks:
1.
2.
3.
Data collection, which comprises 3D scanning each individual’s face, and then taking
a series of photos and one single short video of that same patient;
Data processing, which includes photogrammetric processing of images and video
frames, mesh trimming and registration, separation of meshes as two halves, mirroring
of the left half according to a reference plan, and subsequent calculation of Hausdorff
distances for them;
Statistical analysis, which includes comparing Hausdorff distances for the two halves
of the same model and comparing statistical measures. This analysis was undertaken
by applying statistical coefficients and hypothesis tests.
Figure 1 provides a general scheme for this workflow.
Next, the materials used, the methods employed, and the profile of the volunteer
patients participating in the project will be outlined.
2.1. Materials
2.1.1. Orientation Marks
To establish a set of reference points on the subject’s face, small adhesive markers
were placed on selected facial landmarks, similar to [22] for data collection. Each marker
features a unique, non-repetitive pattern, creating a structured framework that allows the
software to accurately recognize and align the sequence of images during data processing.
In addition, circular retro-reflective marks used for the 3D scanner orientation were placed
on the cap and face.
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Data Collection
Data Treatment
Smartphone
External
Calibration
3D Scanner
Photos
Video
3D Mesh from
Photogrammetric processing
of images
3D Mesh from
Photogrammetric processing
of video frames
3D Mesh after
noise reduction
and cleaning
Trimming
Trimming
Trimming
Cutting in two halves
Mirroring the left half
Statistical Analysis
Hausdorff distances
for pairs of halves
Hypothesis testing
Conclusions
Figure 1. Workflow for the experiment, comprising data collection, data treatment, and
statistical analysis.
2.1.2. Cell Phone Data Acquisition (Static Images and Video)
The smartphone used for image and video captures is considered a reliable mobile device due to its advanced features, including a fast processor, high-quality camera, and built-in image stabilization, which enable the capture of high-resolution images
(4000 × 3000 pixels) and video images (1920 × 1080 pixels) at a refresh rate of 120 Hz and
up to 60 frames per second. Both were captured with the rear-wide angle camera.
The device’s performance, detailed in Table 1 [27], meets the specific requirements of
the project. By replacing a traditional 3D scanner with a cell phone, which is available at a
fraction of the cost, the project opts for a low-cost solution. It is also crucial to highlight the
importance of software–hardware compatibility.
Table 1. Official specifications for the smartphone chosen for this experiment. For data collection
(both photos and videos), the rear-wide camera (specifications in bold) was chosen.
Camera
Specifications
Front-Wide
Rear-Ultra-Wide
10 MP F2.2 [Dual Pixel AF], FOV 80°, 1/3.24”, 1.22 µm
12 MP F2.2 [FF], FOV 120°, 1/2.55”, 1.4 µm
50 MP F1.8 [Dual Pixel AF], OIS, FOV 85°, 1/1.56”, 1.0 µm with
Adaptive Pixel
10 MP F2.4 [3× PDAF], OIS FOV 36°, 1/3.94”, 1.0 µm
3× Optical Zoom Super Resolution Zoom up to 30×
Rear-Wide angle
Rear-Telephoto 1
Rear-Space Zoom
2.1.3. Three-Dimensional Scanner
Academia 50 is a professional-grade 3D digitization tool developed by Creaform Inc.
(Lévis, QC, Canada) that allows the user to achieve precise and reliable results at a vast
number of engineering sectors. For this study, this portable device, based on white light
technology, acts as a reliable data collection approach. Its technical specifications set a
threshold accuracy of 0.250 mm and resolution of 0.250 mm [28]. VXElements was used
as the platform for running the ACADEMIA 50 Scanner. It provides self-positioning with
targets, incorporates geometry and texture, with texture mapping and target filing, and
improves precision through contour optimization [29].
After calibrating, the Academia 50 3D scanner is ready to create registered meshes.
For each scanned patient, it produces a mesh with texture using its integrated camera.
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A structured light source is located at the top, as seen in Figure 2. In the lower central
section is the camera. In the lower right and left sections are the other two structured white
light sources.
Figure 2. Academia 50 3D scanner in use.
2.1.4. Photogrammetry/Videogrammetry
Agisoft Metashape Professional 1.7, developed by Agisoft LLC, is a well-known
software that serves as a tool for creating 3D spatial data based on the photogrammetric
processing of digital images. It stands out, compared to similar software, for its ability to
process considerable amounts of information in a fast and practical manner [30].
For the photogrammetric data collection, three strips were set to cover each patient’s
face. A conventional workflow was used in Agisoft Metashape to achieve 3D models
from both the camera images (photogrammetry) and the video recording (videogrammetry), ranging from data alignment (medium quality) (Figure 3), dense point cloud
(medium quality), and 3D modelling (high quality) without extrapolation and texturing.
For videogrammetry, the video recording was uploaded, and an image extraction of 0.5 s
was set.
Agisoft Metashape utilizes the robust Structure from Motion (SfM) algorithm [31]. Both
images and video frames were processed in an equal manner, initially by importing all images and video frames for alignment/orientation. During the initial alignment/orientation
in Agisoft Metashape, the “Generic Preselection” option was applied, resulting in a sparse
point cloud. For generating the dense point cloud, medium accuracy settings were used,
with a maximum of one million key points and tie points per photo. The point cloud
was, afterwards, filtered for confidence (only points greater than 2). At the end of the
process, a 3D mesh with texture, corresponding to the objects represented in the images
was generated.
2.1.5. CloudCompare, Blender, and MeshLab
CloudCompare and MeshLab manage to solve the necessity of processing software
for triangular meshes [32,33]. The former, developed during a collaboration of Telecom
ParisTech and EDF, and the latter, developed by the ISTI-CNR, are both free open-source
software that are employed indistinctly for the operations requiring mesh processing
and management. Blender is a powerful free and open-source 3D computer graphics
creation suite that is used not only for mesh manipulation but also for animation and
3D modeling [34]. The three computer graphics tools were used for several steps of the
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proposed methodology: Cloud Compare for trimming and registering meshes coming from
all three acquisition methods; Blender for cutting face meshes in two halves according
to specific facial landmarks; and finally, Meshlab for calculating Hausdorff distances,
processing distance heatmaps, and providing statistical measures.
Figure 3. A screenshot of Agisoft Metashape showing image orientation referred to one of the patients
who volunteered for this project.
2.1.6. Patients
Fifteen volunteers acting as prospect patients were scanned for testing the imagebased facial assessment approaches, comprising seven women and eight men—all of them
cisgender. Out of the volunteers, five were born in South America, one in China, and nine
in Europe. Two out of the South Americans and all Europeans were Caucasian, the other
three South Americans were mixed, and the Chinese national was Asian. Age spans varied
from 22 to 43 years old, with a median value of 24. All signed the informed consent form
that was pre-approved as part of this research, thus following the university’s required
protocol for experiments with humans.
2.2. Methods
This section provides a comprehensive breakdown of each step undertaken in alignment with the general framework illustrated in Figure 1. It elaborates on the systematic
acquisition of 3D point clouds, images, and videos, followed by the detailed processing
pipeline. This includes the segmentation of meshes into two halves, the computation of
Hausdorff distances, and the subsequent statistical analysis of the results. Each phase is
described to ensure the clarity and reproducibility of the methodology.
2.2.1. Data Collection
All fifteen patients in their twenties, thirties, and forties, after signing a consent form,
were instructed to wear the coded cap with eight stickers attached to their faces, as shown
in Figure 4.
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Nasion
Tragus
Tragus
Zygion
Zygion
Gonion
Gonion
Pogonion
Figure 4. Diagram showing the landmarks on which stickers were placed.
A more thorough definition of the chosen landmarks, according to [35,36], would be
as follows:
•
•
•
•
•
Nasion: the anatomical point located at the midline of the skull, where the frontal bone
and the two nasal bones intersect;
Tragus: the cartilaginous projection located in front of the external ear canal;
Zygion: the most lateral point on the zygomatic arch, a prominent bony structure on
the side of the skull;
Gonion: the most lateral, inferior point on the angle of the mandible;
Pogonion: the most forward-projecting point on the anterior surface of the mandible.
The placement of the five additional stickers served two primary purposes. First, it
aimed to replicate the distribution of Control Points (CPs) in a photogrammetric block
by covering the edges of the surface to be imaged, thereby ensuring a more robust bundle block adjustment. Additionally, stickers with unique markings assist algorithms in
identifying homologous points in regions of the face where the skin is smooth and lacks
contrast. Conversely, areas near the lips and eyes naturally provide sufficient contrast,
offering plenty of identifiable homologous points in stereoscopic images, making additional stickers unnecessary in those regions. Therefore, the proposed arrangement was
considered sufficient to provide a robust set of easily identifiable homologous points for
photogrammetric processing.
Additional round stickers were primarily applied to the coded cap to enhance the
alignment functionality of the 3D scanner. Their purpose was to improve the quality of
the mesh generated by the scanner but also served as additional high-contrast marks for
photogrammetric processing.
Once fully equipped, each patient was invited to sit in a chair and stand still for data
collection (Figure 5). Firstly, their faces and necks were scanned using the Academia 50
3D scanner, which was calibrated at the beginning of each scanning session. Afterwards,
cell phone pictures were taken from different angles around their face and neck. Later, a
single-shot video was taken, following a pre-determined path around each patient, similar
to the cell phone pictures.
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Figure 5. Patient being scanned with the 3D scanner (left) and the cell phone for images and
video (right).
For both images and videos, a standardized protocol was established to ensure consistency across patients. A minimum of forty-five images was required for each subject:
fifteen following a semicircular path around the upper part of the head, fifteen along a
semicircular path around the individual’s head, and fifteen more along a semicircular path
around the individual’s jaw and neck (Figure 6). The video followed the same path as the
images, captured continuously without interruption. All cell phone images and videos
were taken at a resolution of 4000 × 3000 pixels for static photos and 1920 × 1080 pixels for
video frames, avoiding the highest resolutions supported by the smartphone used for this
experiment: 4 K and 8 K.
Table 2 summarizes the key data collected for each patient. For video processing,
the frames were extracted at a rate of three frames per second, resulting in a number of
photos three times the length of each video in seconds. The procedure went smoothly for
thirteen out of the fifteen patients, with scanning times ranging from four to seven minutes.
However, Patient 11’s voluminous beard posed challenges for the scanner (though not for
the image and video processing), even with round markers placed in various areas of the
beard. Additionally, Patient 8 exhibited significant involuntary facial movements, leading to
some artifacts in the scanned model and a few blurred images and video frames (Figure 7).
Figure 6. Diagram showing the cell phone path for capturing images and video.
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Table 2. Data collection for patients with the ACADEMIA 50 3D scanner, and the smartphone in
camera and video mode.
Patient
3D Scanning Time
Photogrammetric Time
Number of Images
Videogrammetry
Time
Number of Video
Frames
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
5 min
6 min
5 min
7 min
5 min
4 min
6 min
13 min
7 min
5 min
11 min
5 min
4 min
6 min
5 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
1 min
52
51
50
48
47
51
47
56
65
53
50
47
48
47
53
48 s
52 s
49 s
57 s
47 s
47 s
49 s
45 s
59 s
35 s
63 s
47 s
47 s
45 s
61 s
144
156
147
171
141
141
147
135
177
105
189
141
141
135
183
Figure 7. Close-up of Patient 8’s mouth. It is possible to notice how involuntary movements lead
to poor quality meshes, especially for the ACADEMIA 50 3D scanner (c); it is less apparent in
photogrammetry (a) and videogrammetry (b) 3D models.
2.2.2. Data Processing
This process resulted in three 3D models per volunteer: a scanner-generated mesh, a
mesh derived from photogrammetry, and another from videogrammetry. Each mesh was
then trimmed in CloudCompare to retain only the region corresponding roughly to the
person’s face. Since the photogrammetric meshes were referenced to arbitrary coordinate
systems, they were registered to the scanner mesh by applying a scale transformation,
ensuring that all three meshes for each subject shared a consistent coordinate system
(Figure 8).
Figure 8. Three-dimensional models for Patient 4 side-by-side: (a) 3D scanner, (b) photogrammetry,
(c) videogrammetry.
Once all meshes were properly trimmed and registered, Hausdorff distances were
calculated as a potential measure of asymmetry between the two halves of a face. The
Hausdorff distance is a measure of the maximum of the closest in two sets of points,
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commonly used to assess shape similarity by measuring errors in creating a triangular
mesh to approximate a surface [37,38]. There are alternatives for calculating distances
between objects, such as Root Mean Squared Error (RMSE), Chamfer distance [39], and
Procrustes analysis [40]. However, the Hausdorff distance was chosen because it serves
as a measure of dissimilarity between two sets of points, hence being a useful computer
graphics tool for determining degrees of asymmetry between two halves of the same face.
Ref. [41] provides the following definition:
Let A, B ⊂ Rn . The unilateral Hausdorff distance between A and B is calculated
as follows:
d( A → B) = sup in f | x − y|
(1)
x ∈ A y∈ B
And the Hausdorff distance is defined by the following:
d H ( A, B) = max (d( A → B), d( B → A))
(2)
For the calculation of Hausdorff distances, all meshes were cut into two halves with
a vertical plane. The calculation was carried out using Meshlab 2023.12 software and its
built-in Hausdorff distance algorithm [42]. According to [43], ideally, the top of the head
and the center of the chin would be used. However, since all volunteers wore the cap, their
hairline and part of their forehead were covered. Thus, the references used for splitting
faces into two halves were the pogonion, pronasale, and glabella, as shown in (Figure 9).
This step was performed using Blender 4.2 software [34] with native algorithms.
The second stage involved reflecting the left, mirroring it according to the vertical
plane and then contrasting both for dissimilarity comparisons. Finally, the Hausdorff
distance was calculated for each pair of face halves with a graphical representation of the
shortest distances between two points of the meshes as a heatmap (Figure 10). This step
was also entirely developed using Meshlab 2023.12 software [42].
Splitting
Glabella
Mirroring
Asymmetries
Pronasale
Pogonion
Mark 802
Figure 9. Facial landmarks defining the plane to be used for cutting face models in two halves (shown
in the diagram in green and red). The model is cut according to the plane defined by those marks;
then, its left part is mirrored and the asymmetries between both surfaces are calculated.
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Figure 10. Hausdorff distances between the two halves (for the photogrammetry models of Patient
15) represented as a heatmap with higher distances in blue and lower distances in red (units in mm).
These procedures, as described above, produce datasets composed of heatmaps describing which areas of the individual’s face are more asymmetrical than their equivalent
on the other side. These heatmaps were then converted into a series of histograms containing frequencies of each distance and heatmaps on the specific dissimilar areas. For each
histogram, statistics such as mean, median, and standard deviation were also calculated.
In both cases, the approach included analyzing their linear association through the
Pearson Product–Moment Correlation and conducting hypothesis tests on the mean, median, and variance of such histograms.
2.2.3. Statistical Analysis
The Pearson Product–Moment Correlation can be understood as a measure of the
degree of linear relationship between two random variables. Therefore, the correlation coefficient emphasizes predicting the degree of dependence between two random
variables [44,45]. Its estimator is defined by the following:
corr =
cov xy
s x sy
(3)
where
cov xy is the sample covariance;
s x is the sample standard variation for the independent variable (in other words,
variable x);
sy is the sample standard variation for the dependent variable (in other words, variable
y as a function of x: y = f ( x )).
A strong positive correlation, reflecting a high degree of dependency between variables (and their similarity), results in values close to 1. Values near zero suggest little to no
linear relationship between the variables. Negative values, down to −1, indicate an inverse
relationship, meaning that as one variable increases, the other decreases. For this procedure,
Pearson’s Product–Moment Correlation coefficient was computed to evaluate pairwise
correlations between the means of Hausdorff distances of meshes generated via photogrammetry, videogrammetry, and 3D scanning. High correlations were anticipated under the
assumption that comparable measurement techniques would yield consistent results.
The Paired t-Test is employed to compute mean differences when observations from
two populations of interest are collected in pairs [46–48]. This test examines the differences
between two observations from the same subject taken under similar conditions. It is
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a specific instance of the two-sample t-Test, which applies to samples with unknown
population means and standard deviations but is assumed to follow a normal distribution.
It produces a T-Stat that must be smaller than the T-Critical. It also produces a p-value
which must be larger than the alpha value and reflects the probability of obtaining the
observed results, assuming that the null hypothesis is true. In this scenario, the analysis
was conducted to determine whether statistically significant differences exist between the
means of Hausdorff distances of meshes produced by photogrammetry, videogrammetry,
and 3D scanning, evaluated pairwise.
When testing means of two groups, for similarity, its statistic can be simplified
as follows:
D̄
T= S
(4)
√D
n
The hypotheses are formulated as follows:
H0 : µ1 = µ2 ;
H1 : µ1 ̸= µ2 ;
H1 : µ1 > µ2 ;
H1 : µ1 < µ2 ,
where
µ1 and µ2 are the hypothetized means of the two paired groups;
D̄ is the mean of the differences between the two samples;
SD is the standard deviation of the differences between the two samples;
n refers to the size of the sample.
The Repeated Measures Analysis of Variance (rANOVA) is employed to determine
if there is a statistically significant difference between the means of three or more groups
related to measures taken from the same subjects [49–51]. It is, therefore, an extension of the
Paired t-Test. Similarly, it yields a F-Stat which must be smaller than the F-Critical, and a
p-value that must be larger than the alpha value. Therefore, it complements the Paired t-Test
in that it helps determine whether statistically significant differences exist between the
means of Hausdorff distances of meshes produced by photogrammetry, videogrammetry,
and 3D scanning, evaluated together as a group. Its statistic is defined by the following:
Its statistic is given below:
F=
MSgroup
MSerror
(5)
The hypotheses are formulated as follows:
H0 : µ1 = µ2 = µ3 = ... = µk ;
H1 : At least two hypothesized means are statistically different,
where
MSgroup is the mean squared error of between-group variance;
MSerror is the mean squared error of within-group variance;
The Wilcoxon–Mann–Whitney (or simply Mann–Whitney) test may be used when two
samples are different from the normal distribution but have similar distribution shapes and
variances. It is especially useful for assessing the medians of two groups. For this test, the UStat must be larger than the U-Critical. For this study, it was employed to determine whether
statistically significant differences exist between the medians of Hausdorff distances of
meshes produced by photogrammetry, videogrammetry, and 3D scanning, evaluated
pairwise. The test uses the ranks of measurements in the following manner [52]:
N = n1 + n2
U1 = n1 n2 +
n1 ( n1 + 1)
− R1
2
(6)
(7)
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U2 = n2 n1 +
n2 ( n2 + 1)
− R2
2
U = min(U1 , U2 )
(8)
(9)
The hypotheses are formulated as follows:
H0 : The hypothetised measures are not statistically different;
H1 : The hypothetised measures are statistically different,
where
n1 and n2 are the number of observations in samples 1 and 2;
R1 and R2 are the sum of the ranks of the observations in samples 1 and 2.
Finally, the Kruskal–Wallis test is, in a certain way, a non-parametric equivalent to
ANOVA. According to [53], it determines if independent groups have the same mean on
ranks, instead of the data themselves. For that reason, it may be used to assess medians
of more than two samples. Parallel to the Mann–Whitney test, it was suggested to verify
whether statistically significant differences exist between the medians of Hausdorff distances of meshes produced by photogrammetry, videogrammetry, and 3D scanning, this
time, evaluated all at once. It requires a sample size of 5 or more and provides a H-Stat and
a p-value that must be larger than the alpha value. Its statistic is given by the following:
H=
k
Ri 2
12
− 3( N + 1)
∑
N ( N + 1 ) i =1 n i
(10)
The hypotheses are formulated as follows:
H0 : The hypothesized measures are not statistically different;
H1 : At least two hypothesized measures are statistically different,
where
ni is the size of sample i;
N is the total sample size;
k is the number of groups being compared;
Ri is the sum of the ranks of the observations in sample i.
The four aforementioned tests, in that regard, extensively compare meshes of the
same subject in pairs and as a group. They provide statistically significant answers that
help evaluate if meshes obtained from photogrammetry and videogrammetry are just as
effective as meshes obtained from the 3D scanner counterpart—but with a special emphasis
on determining facial asymmetries among two face halves.
3. Results
3.1. Histograms and Measures of Central Tendency
For all subjects, the spatial distribution of Hausdorff distances was graphically rendered (as shown, for example, in Figure 11).
The initial visual analysis indicates that, in certain regions of each patient’s face, the
models show notable agreement, though some discrepancies appear, particularly in the
models derived from videogrammetry. Since these differences are within the millimeter range, histogram analyses offer a clearer depiction of each model’s performance in
identifying asymmetries. These histograms are presented in Figure 12.
Overall, the histograms demonstrate similar shapes and widths across the models. For
patients with more pronounced asymmetries, such as Patients 3, 4, 6, and 10, the histograms
appear broader, reflecting greater variation. Conversely, for more symmetrical patients,
such as Patients 2, 7, and 14, the histograms are narrower. While the general shapes align
across sensors, some variations occur, with one sensor occasionally showing multiple peaks
compared to others. This pattern is particularly evident for Patient 8, whose histogram
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displays multiple peaks at various frequencies, likely attributable to involuntary facial
movements, as previously discussed.
Figure 11. Spatial distribution of Hausdorff distances (in mm) between the two halves of Patient 13’s
face, according to the models derived from (a) photogrammetry, (b) videogrammetry, and (c) the 3D
scanner. Areas in blue are more asymmetrical, whereas areas in red are much more symmetrical.
In order to develop a more quantitative analysis, and determine if such small variations
are statistically significant, the following measures of central tendency were calculated for
each patient: the mean, the median, and the standard deviation. They are summarized in
Table 3 and in Figure 13.
The data suggest that, for most patients, the means and medians are quite consistent
across the three sensors, with a discrepancy of 1 mm or less between these measures.
However, Patient 8 exhibits a more notable variation, particularly between the means and
medians from the videogrammetry and 3D scanner histograms, which suggests potential
data collection limitations that may affect this study’s reliability for this patient.
Moreover, patients with more asymmetrical facial structures tend to have a higher
standard deviation, as observed in the histograms, whilst more symmetrical patients exhibit
a notably lower standard deviation.
To deepen the analysis, hypothesis tests were performed to assess whether the datasets
from the three sensors differed significantly from each other. This approach aims to clarify
whether any observed differences are statistically meaningful.
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Figure 12. Frequency histograms for Hausdorff distances for the two halves of each patient. For each
histogram, the mean is represented as a dotted black line, standard deviations are represented as
dotted red lines and the median is represented as a dotted green line.
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Table 3. Measures of central tendency in mm (rounded to one decimal place) based on the histograms of Hausdorff distances between face halves for each patient, for models generated from
photogrammetry, videogrammetry, and 3D scanning.
Photogrammetry
Videogrammetry
3D Scanner
Patient
X̄
Med
sX
X̄
Med
sX
X̄
Med
sX
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
2.1
2.0
4.9
3.7
2.0
2.9
2.7
2.4
0.8
3.0
2.3
2.0
2.0
1.7
3.0
1.8
1.8
4.5
3.0
1.4
2.1
2.7
2.1
0.6
2.7
1.7
1.7
1.5
1.6
2.6
1.9
1.9
4.8
2.8
1.9
2.8
2.2
2.0
0.8
2.0
2.0
1.5
1.7
1.3
2.3
1.8
1.9
3.8
2.5
2.2
3.1
1.6
1.5
1.7
3.7
3.2
2.6
2.0
1.2
2.4
1.3
1.6
3.1
2.1
1.4
2.7
1.0
1.1
1.4
2.6
2.8
1.6
1.7
0.9
2.3
1.5
1.6
2.5
1.3
2.3
2.2
1.7
1.4
1.3
3.2
2.5
2.7
1.7
1.0
1.2
1.5
0.9
4.2
3.2
1.8
3.8
1.9
4.3
1.8
4.1
2.3
2.0
2.9
0.8
2.9
1.3
0.6
3.7
2.7
1.3
3.6
2.0
3.8
1.5
4.3
2.0
1.6
2.6
0.6
2.5
1.2
1.1
2.3
2.4
1.4
3.6
1.1
3.0
2.1
3.5
1.7
1.4
2.4
0.7
2.5
Figure 13. Data extracted from Table 3, as a box plot.
3.2. Hypothesis Tests
Three two-tailed paired t-tests were performed at a 95% confidence level to compare
the mean values of the histograms obtained from each method: one test compared photogrammetry with 3D scanning, a second compared videogrammetry with 3D scanning,
and a third compared photogrammetry with videogrammetry.
The hypotheses for these tests were as follows:
H0 : The means are not statistically different.
H1 : The means are statistically different.
The outcomes for these tests are presented in Table 4.
Table 4. Paired t-Tests for the mean values of the histograms obtained from each method.
Photogrammetry versus 3D Scanner
Videogrammetry versus 3D Scanner
Photogrammetry versus Videogrammetry
corr
T
T-Critical
(Two-Tailed)
p-Value
Recommendation
0.663
0.604
0.652
−0.276
−0.893
0.794
2.145
2.145
2.145
0.787
0.387
0.440
Do not reject H0 .
Do not reject H0 .
Do not reject H0 .
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The results indicate that all three pairs of datasets exhibit a relatively high linear correlation, suggesting that the mean values of the histograms for the fifteen patients increase
or decrease in a similar pattern. The Paired t-Test results do not provide strong evidence
against the null hypothesis. p-values have also been considerably high, especially when
comparing photogrammetry with 3D scanner data. Therefore, at the 95% confidence level,
we cannot conclude that there is a statistically significant difference between the means.
Additionally, a repeated measures MANOVA (rMANOVA) at a confidence level of
95% was conducted to compare the mean histogram values obtained from each method
simultaneously. The hypothesis for this test were as follows:
H0 : The means for all three sets are not statistically different.
H1 : At least two of the means are statistically different.
A summary of the results is presented in Table 5.
Table 5. rMANOVA test for the mean values of the histograms obtained from all three methods.
F
F-Critical
p-Value
Recommendation
0.497
3.340
0.614
Do not reject H0 .
Consistent with the Paired t-Test results, the rMANOVA does not indicate a statistically
significant difference between the means across methods, even when analyzed simultaneously.
For the medians, two types of non-parametric tests were conducted. Firstly, a Wilcoxon
Mann–Whitney test at a 95% confidence level was used to compare the medians of the
histograms obtained from each method following the same pairs used for the Paired t-Tests.
The hypotheses for these tests were as follows:
H0 : The medians are not statistically different.
H1 : The medians are statistically different.
Parallel with these paired tests, the three sets of medians went through a Kruskal–
Wallis test—also at a 95% confidence level—with the following hypotheses:
H0 : The medians for all three sets are not statistically different.
H1 : At least two of the medians are statistically different.
Tables 6 and 7 summarize the main findings of both tests.
Table 6. Wilcoxon–Mann–Whitney test for the median values of the histograms obtained from
each method.
Photogrammetry versus 3D Scanner
Videogrammetry versus 3D Scanner
Photogrammetry versus Videogrammetry
U
U-Critical
Recommendation
98
75
75
64
64
64
Do not reject H0 .
Do not reject H0 .
Do not reject H0 .
Table 7. Kruskal–Wallis test for the median values of the histograms obtained from all three methods.
H
p-Value
Recommendation
4.475
0.107
Do not reject H0 .
The results from both types of tests indicate no statistically significant difference
between the medians across methods, regardless of whether they are analyzed in pairs or
as a group.
Finally, another rMANOVA test was conducted, this time among the variances for the
three histograms. It also fails to reject the null hypothesis, thus not able to determine any
significant difference between variances for each subject in the three datasets (Table 8).
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Table 8. rMANOVA test for the variances of the histograms obtained from all three methods.
F
F-Critical
p-Value
Recommendation
0.516
3.340
0.603
Do not reject H0 .
3.3. Hypothesis Tests After Removing Patient 8
Due to Patient 8’s tendency for involuntary facial movements, which contributed
to greater variation in means and medians, the analyses were repeated with Patient 8’s
data excluded. The updated statistics are presented in Tables 9–13. In the following
tables, Pearson’s correlation coefficients and p-values with an increase are shown in bold.
Overall, such metrics generally increased, with a single exception (the p-value for the
“photogrammetry versus 3D scanner” Paired t-Test for the means). This suggests that the
results are even more robust without Patient 8’s data.
Table 9. Paired t-Tests for the mean values of the histograms obtained from each method—without
Patient 8.
Photogrammetry versus 3D Scanner
Videogrammetry versus 3D Scanner
Photogrammetry versus
Videogrammetry
corr
T
T-Critical
(Two-Tailed)
p-Value
Recommendation
0.747
0.839
0.357
−0.179
2.160
2.160
0.727
0.861
Do not reject H0 .
Do not reject H0 .
0.672
0.510
2.160
0.618
Do not reject H0 .
Table 10. rMANOVA test for the mean values of the histograms obtained from all three methods—
without Patient 8.
F
F-Criticalb
p-Value
Recommendation
0.152
3.369
0.860
Do not reject H0 .
Table 11. Wilcoxon Mann–Whitney test for the median values of the histograms obtained from each
method—without Patient 8.
Photogrammetry versus 3D Scanner
Videogrammetry versus 3D Scanner
Photogrammetry versus Videogrammetry
U
U-Critical
Recommendation
135
124
139
55
55
55
Do not reject H0 .
Do not reject H0 .
Do not reject H0 .
Table 12. Kruskal–Wallis test for the median values of the histograms obtained from all three
methods—without Patient 8.
H
p-Value
Recommendation
0.605
0.739
Do not reject H0 .
Table 13. rMANOVA test for the variances of the histograms obtained from all three methods—
without Patient 8.
F
F-Critical
p-Value
Recommendation
0.472
3.369
0.629
Do not reject H0 .
4. Discussion
The present study considered a 3D white scanner as reference data to compare the cell
phone photogrammetry and cell phone videogrammetry approaches. The ACADEMIA
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50 3D scanner is a qualified scanner, but not a high-end system of its class. In fact, in
the literature, stationary scanners, such as the Danae 100SP, 3dMDface, and Vectra M3,
are considered to yield real ground-truth datasets [54]. Nevertheless, this portable 3D
scanner yielded the best results in 8 out of 15 patients (cf. Table 3 and Figure 13); similarly,
videogrammetry worked best in 5 out of 15 patients, and photogrammetry in 3 out of
15 patients. Inversely, the portable 3D scanner also worked worst in 4 out of 15 patients,
due to involuntary facial movements, as pointed out in [54]; photogrammetry in 3 out of
15; and videogrammetry in 2 out of 15.
The results presented herein with photogrammetry and videogrammetry worked
well due to the reference frame set by the coded cap and stickers, as reported in [19]. The
coded cap helps to minimize errors when automatically locating homologous points on
the patient’s head, which often has similar textures and patterns. Moreover, the success
of videogrammetry in producing accurate models under certain circumstances can be
attributed to unique features of specific patients, which may mirror real-world conditions.
For instance, Patients 6 and 11 had nose rings, which posed challenges for the scanner. In
the case of Patient 4, data collection appeared to proceed smoothly, yet flaws were observed
in the nose region of the scanned model, suggesting possible involuntary movements
during scanning. Similarly, Patient 15 exhibited similar issues. Additionally, Patient 11 had
a thick beard that the scanner failed to capture effectively, as previously noted.
In each of these cases, videogrammetry produced superior results, yielding smoother
models with fewer vertices while preserving essential facial features. Photogrammetry
outperformed the other methods in only two instances. For Patient 13, the results were comparable to videogrammetry, and Patient 10 displayed a clear advantage with photogrammetry. However, photogrammetry models were generally rougher, with minor bumps on the
cheeks—likely due to image misalignment and residual errors in the point cloud, despite
adherence to a strict image processing protocol. Another factor in videogrammetry’s superior performance could be the stable image capture and consistent focal distance maintained
throughout video acquisition, which supports robust photogrammetric processing.
Statistically, all three modeling techniques were comparable in detecting facial asymmetries. This suggests that even models derived from cell phone photogrammetry may
offer sufficient accuracy for the objectives of this study.
Patient 8 was the only case where the results varied significantly across the three
models, likely due to the patient’s pronounced facial movements, noticeable even during
data collection. Excluding this patient’s data further improved statistical reliability. These
findings highlight opportunities for refining photogrammetric processing of cell phone
images in this specific context.
The initial methodology for this study focused on using the Hausdorff distance as
a measure to evaluate whether state-of-the-art photogrammetric/videogrammetric approaches could accurately capture the level of dissimilarity between the two halves of a
patient’s face. While not intended as a definitive metric for assessing facial asymmetry,
the Hausdorff distance provided an initial benchmark to determine if cell phone-based
photogrammetric methods could approximate the accuracy of 3D scanning in detecting
asymmetries. This metric allowed us to achieve a direct comparison across different sensors.
Herein, the cell phone was used to carry out stereophotogrammetry with great flexibility. It is substantially faster (5 to 13 times) than portable 3D scanning (Table 2). Videogrammetry, taking the same capture time as photogrammetry or slightly shorter, seems to be the
most promising approach to recording facial asymmetries in adults, after portable 3D scanning, outperforming this latter technology for patients with involuntary facial movements.
This statement will be further developed with higher resolution video recording, selecting
4 K or 8 K image recording.
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Also, it is crucial to consider that the proper registration of models derived from stateof-the-art photogrammetric and videogrammetric approaches is a key factor. Commercial
solutions may produce 3D models with varying coordinate references and scale factors. In
this study, model registration was performed manually, potentially introducing additional
errors, even though the final analyses were reasonably accurate. This issue is particularly
evident in the Hausdorff distance analysis, where mismatches between face halves may
have resulted in poorer results, especially around the edges where the meshes did not
fully align. Therefore, developing a solution that ensures precise model registration and
references to create a consistent metric system is a primary objective for this project.
The scale factor that might affect the photogrammetric and videogrammetric performance has been omitted in the research presented herein. Further research is needed to
understand how this methodology applies across different cell phone models, which may
produce meshes with varying orientations and scale factors [20].
What is clear, however, is the viability of using cell phone photogrammetric and
videogrammetric 3D models for measuring facial asymmetries in old patients (not newborns). The hypothesis tests that were applied strongly and unequivocally suggest that 3D
models obtained from cell phone photogrammetry and videogrammetry have proven to be
similar to those produced by portable 3D scanners, making them a cost-effective alternative
for medical practitioners and the public healthcare system, that may not opt for high-end
synchronized multi-camera or multi-scanners systems.
Also, to measure facial asymmetry, the choice between Euclidean distance, Hausdorff
distance, and comparison of specific anatomical landmarks depends on the desired level of
precision and the specific needs of the study. The Euclidean distance is simple and suitable
for quick analyses, but it may not capture all the nuances of a complex facial surface [55].
The Hausdorff distance is more robust and detects small variations, making it ideal for
complex facial shapes [56]. Comparing specific anatomical landmarks provides a detailed
and specific analysis of facial asymmetry, but it is more complex and prone to errors. Each
method has its advantages and limitations, and the choice should be influenced by the
context of facial asymmetry study. What remains evident is the need to properly measure
facial asymmetries, which are visual markers of a series of pathologies [57]. Combining
Hausdorff distances with the comparison of anatomical landmarks can be a good alternative
for measuring small facial asymmetries important to detect in dysmorphology studies.
Finally, it is important to point out the limitation of the sample size, which consisted
of fifteen individuals. The decision to conduct a small-scale exploratory study was driven
by the need to refine the methodology and establish preliminary parameters. Despite this
limitation, the study involved forty-five distinct meshes and required extensive computational efforts, particularly when comparing the spatial distributions of Hausdorff distances
between points of each mesh after being clipped into two halves. The primary objective
was to evaluate whether cell-phone images—captured using both the photographic camera
and the video camera—could generate 3D models of human faces with sufficient accuracy
to assess facial asymmetries, employing photogrammetry for still images and videogrammetry for video footage, both facilitated by a coded cap. The findings demonstrate that
3D models derived from cell phone images exhibit a high linear correlation with those
produced by precise portable 3D scanners. It is worth noting that this study focused on
comparing methodologies rather than analyzing the clinical parameters of the participants.
These promising results provide a strong foundation for the next step: validating the
proposed method in a larger population. This is considered to be particularly significant
given the approach cost-effectiveness, which could reduce the financial burden on public
health systems.
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5. Conclusions
This investigation aimed to determine whether cell phone images from either the
photographic camera or the video camera can be used to create 3D models of human faces
that are accurate enough to assess facial asymmetries using photogrammetry for the former
and videogrammetry for the latter, all together with a coded cap. The findings indicate
that 3D models generated from cell phone images exhibit a high linear correlation with
those obtained from accurate portable 3D scanners. However, the final results depend on
the technology used, and not only on the accuracy of the system; stability and reliability
also depend on the behavior of the patient, i.e., involuntary movements of the patient to
choose the technology, 3D scanning vs. image-based photogrammetry/videogrammetry.
Moreover, the analysis supports the claim that, when analyzing means, medians, and
standard deviations, through a series of distinct hypothesis tests and with a 95% confidence
level, there is no significant dissimilarity between these 3D cell phone models and those
achieved with accurate and expensive portable 3D scanners. The flexibility of handle cell
phones is demonstrated, and sometimes, videogrammetry outperforms (4/15 = 26.7/100)
both 3D scanning and photogrammetry, with only a limited number of down performances
(2/15 = 13.3/100).
While the investigation results show a high correlation between 3D cell phone models
and portable 3D scanners, it is important to consider the possible biases and limitations of
this approach. The accuracy of the models heavily depends on the quality of the cell phone
cameras and the control over patient movements during image capture [58]. Furthermore,
comparing 3D models generated by different technologies can introduce variabilities not
accounted for in the statistical tests [59]. Combining different methodologies, such as
Hausdorff distance [60] and the comparison of anatomical landmarks, could improve the
detection of subtle facial asymmetries, but this approach also requires greater precision
in landmark marking and may be more susceptible to measurement errors. Therefore,
future studies should focus on optimizing and validating these methodologies to ensure
consistent and applicable results in clinical practice.
For future studies on facial asymmetry, alternative metrics will be explored, such as
measuring distances or ratios between key facial landmarks to discern anthropometric
metrics; alternatively, coded targets might also be used to increase the accuracy [21].
Additionally, subdividing 3D models into smaller sections based on specific landmark
sets [61] could provide more granular insights into asymmetry indices. Future research
will expand the sample to encompass a broader age range, including a more stratified
representation across gender groups [62], given that facial asymmetries are more prevalent
among older individuals and cisgender males. Furthermore, focusing on specific facial
regions that are particularly prone to asymmetry and biometric analyses is a challenge that
will allow us to evaluate the accuracy and reliability of the proposed methodology in these
targeted patients.
Author Contributions: Conceptualization, L.C.T.C., O.C.Q.-E. and J.L.L.; methodology, L.C.T.C.,
O.C.Q.-E., J.L.L., M.F.C.P., F.M.d.C. and A.L.M.M.F.; software L.C.T.C., J.L.L., M.F.C.P. and F.A.A.;
validation, L.C.T.C., M.F.C.P. and F.A.A.; formal analysis, L.C.T.C., O.C.Q.-E. and J.L.L.; investigation,
L.C.T.C., O.C.Q.-E. and J.L.L.; resources, J.L.L.; data curation, L.C.T.C. and J.L.L.; writing—original
draft preparation, L.C.T.C., M.F.C.P., F.M.d.C., A.L.M.M.F. and F.A.A.; writing—review and editing,
O.C.Q.-E. and J.L.L.; visualisation, L.C.T.C. and J.L.L.; supervision, L.C.T.C. and J.L.L.; project
administration, L.C.T.C. and J.L.L.; funding acquisition, L.C.T.C. and J.L.L. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by Asociación de Científicos Españoles en Brasil (ACEBRA),
Fundación Ramón Areces, the Centre de Cooperació al Desenvolupament (CCD—Cooperació 2023)
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from the Universitat Politècnica de València, and last but not least, the Instituto de Salud Carlos III
under project number PI22/01416 and joint financing by the European Union.
Institutional Review Board Statement: This study was conducted according to the guidelines of
the Declaration of Helsinki, and approved by the Ethics Research Committee of the Universitat
Politècnica de València (protocol code no. P03-29-04-2022 on 27 July 2023).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available upon request. Please
contact the corresponding author.
Acknowledgments: The authors acknowledge the contributions of the GIFLE team at the Universitat
Politècnica de València (especially Juan José Valero Lanzuela and Miriam Cabrelles López), of the
LFSR team at Universidade do Estado do Rio de Janeiro (especially Luiz Felipe de Almeida Furtado,
Bruna da Costa Alves, Mateus Álvares Sousa and Ygor Demetrio Pereira Dias da Costa), of the LEMC
team at Instituto Oswaldo Cruz (especially Fernando Regla Vargas and Bruna dos Reis) and of the
Center for Technological Innovation at Fiocruz (Aline Morais and Ana Carolina Carvalho). Matheus
Ferreira Coelho Pinho receives a PhD scholarship from Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior, Brazil (Process: 88887.849129/2023-00), for the PhD Programme of Computational
Sciences and Mathematical Modeling at the Rio de Janeiro State University. Ana Luiza Meneguci
Moreira Franco receives a scholarship from Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior, Brazil (Process: 88887.832293/2023-00), for the PhD Programme in Genetics at the Federal
University of Rio de Janeiro. Francisco Airasca Altónaga is a Mechanical Engineering student at UPV,
Spain, and received a Centre de Cooperació al Desenvolupament MERIDIES scholarship to study as
an exchange student at the Rio de Janeiro State University.
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
3D
CP
GIFLE
LEMC
LFSR
rANOVA
UPV
Three-Dimensional
Control Point
Grupo de Investigación en Fotogrametría y Laser Escáner
Laboratório de Epidemiologia das Malformações Congênitas
Laboratório de Fotogrametria e Sensoriamento Remoto
Repeated Measures Analysis of Variance
Universitat Politècnica de València
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