Understanding the Impact of Image Quality in Face Processing
Patricia Alejandra Pacheco Reina
, Armando Manuel Gutiérrez Menéndez
José Carlos Gutiérrez Menéndez
, Graça Bressan
and Wilson Ruggeiro
Polytechnic School, University of São Paulo, São Paulo, Brazil
Laboratory of Computer Networks and Architecture, University of São Paulo, São Paulo, Brazil
Keywords: Face Image Quality, Face Processing, Image Distortions.
Abstract: Face processing algorithms are becoming more popular in recent days due to the great domain of application
they can be used in. As a consequence, research about the quality of face images is also increasing. Several
papers concluded that image quality does impact the performance of face processing algorithms, with low-
quality images having a detrimental effect on performance. However, there is still a need for a comprehensive
understanding of the extent of the impact of specific distortions like noise, blur, JPEG compression, and
brightness. We’ve conducted a study evaluating the performance of three face processing algorithms with
images under different levels of the aforementioned distortions. The study’s results placed noise and blur with
Gaussian distributions, as the main distortions affecting performance. A detailed description of the adopted
methodology, as well as the results obtained from the study, is presented in this paper.
In 2020, an increase in the use of efficient face
processing algorithms was evidenced due to the
demand for this technology in many services that
require a type of personal identification. This was due
to the social distancing and confinement caused by
the epidemiological issues related to the COVID-19
virus worldwide. Face processing technology is
widely used for security and access control through
identification, verification, and liveness processes.
Other methods like gender classification, age
estimation, and emotion detection are also gaining
attention thanks to their application in advertising and
recommendation systems. As a consequence,
research about the quality of face images is also
increasing, with the general consensus being that
image quality is an important factor in the
performance of face processing algorithms.
A recent study by (Mehmood and Selwal, 2020)
made a review about face recognition methods and
the factors affecting their accuracy. The study divided
the algorithms into appearance-based methods,
feature-based methods, and hybrid methods; and
evaluated their strengths and limitations while listing
the main factors affecting face recognition.
According to the authors, the main factors related to
image quality affecting face recognition performance
are illumination, occlusion, noise, and low-
In a survey by (Li et al., 2019) about image quality
in face recognition, the authors stated that the main
challenges lay in the first stages of the face
recognition pipeline: face detection and face
alignment. According to this survey, face detection is
particularly impacted by low-resolution images, and
for the case of face alignment, the best performing
algorithms aren’t trained to consider image
distortions, so it could be concluded that in the
presence of low-quality images, their performance
will suffer.
A paper by (Jaturawat and Phankokkruad, 2017)
evaluated the face recognition accuracy of three well-
known algorithms: Eigenfaces (Turk and Pentland,
1991), Fisherfaces (Belhumeur, Hespanha and
Kriegman, 1997), and LBPH (Chen et al., 2009),
under unconstrained conditions, considering a variety
of pose and expressions, as well as different light
exposures, noise levels, and resolution. All three
algorithms showed poor performance across the
Research has also been conducted to tackle this
issue outside of the face recognition domain. In
Reina, P., Menéndez, A., Menéndez, J., Bressan, G. and Ruggeiro, W.
Understanding the Impact of Image Quality in Face Processing Algorithms.
DOI: 10.5220/0010486501450152
In Proceedings of the International Conference on Image Processing and Vision Engineering (IMPROVE 2021), pages 145-152
ISBN: 978-989-758-511-1
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
(Mahmood et al., 2019), the authors placed occlusion,
illumination, and noise as the main factors affecting
facial expression recognition in unconstrained
environments. Similarly, a paper by (Kang et al.,
2018), concluded that optical and motion blur
negatively affect the performance of age estimation
Relevant work on the topic of image quality was
conducted by (Dodge and Karam, 2016). The authors
studied the effects of several distortions on the
performance of four deep learning architectures
focused on image classification. The authors
concluded that Gaussian blur and Gaussian noise had
the biggest impact on deep learning architectures,
while the other distortions affected to a lesser degree.
The literature available on this matter supports the
premise that image quality does influences face
processing performance. However, there is still a lack
of comprehension about the impact of specific image
distortions. With the exception of resolution, whose
impact is been greatly researched and documented (Li
et al., 2019), our knowledge about other distortions’
impact on face processing algorithms is limited. We
know that images degraded by distortions such as
noise, blur, lack or excess of brightness, etc, might be
poorly processed by these algorithms, as is outlined
in the papers above. However, a deeper understanding
of that impact and the extent to which it is relevant for
face processing would be useful to accurately address
this issue and propose adequate solutions.
With that motivation, we’ve conducted a study to
further comprehend the impact of image quality in
face processing algorithms. Three face processing
algorithms were tested with images under different
levels of Gaussian noise, Gaussian blur, motion blur,
low and high brightness, and JPEG compression. The
results of the study are presented in this paper.
Section 2 describes the adopted methodology, Section
3 presents the results obtained with each type and
degree of distortion, and Sections 4 and 5 outline the
summary and the conclusions of the study.
The methodology adopted for the study is based on
the work of (Dodge and Karam, 2016). However, a
few changes were made to adapt it to our goal. The
main differences in our approach are that the selected
algorithms are focused on different tasks as opposed
to one, and that each algorithm was tested with a
dataset and a set of metrics corresponding to the task
in question. Also, three additional distortions were
considered as a part of our study: motion blur, low
brightness, and high brightness.
Details about the algorithms, the datasets, the
metrics, and the distortions are discussed below.
2.1 Face Processing Algorithms
The algorithms evaluated in the study are FaceNet,
Deep Age Estimation (DEX), and Deep Alignment
Network (DAN), focused on face recognition tasks,
age estimation, and face alignment respectively.
These algorithms are based on Deep Learning
architectures and have achieved state-of-the-art
results in their respective tasks.
FaceNet is a deep learning system that generates
face embeddings for face recognition tasks, such as
face identification and face verification, proposed by
(Schroff and Philbin, 2015). The main contribution of
this work is the introduction of a new loss for deep
learning architectures, specifically made for face
recognition purposes: the triplet loss. FaceNet uses
two DCNN as base architectures: the Zeiler&Fergus
(Zeiler and Fergus, 2014) style networks and the
Inception (Szegedy et al., 2015) type networks. For
this study, an implementation of the FaceNet system
based on the Inception architecture was chosen, and
the algorithm’s performance was evaluated using the
accuracy, and the validation rate under a fixed False
Alarm Rate (FAR) of 0.001.
The DEX algorithm consists of a deep learning
architecture for apparent and real age estimation
from a single face image and without the use of
facial landmarks (Rothe, Timofte and Van Gool,
2018). The pipeline of the entire system consists of
four main stages: face detection, face alignment and
resize, feature extraction, and age estimation. To
measure the model’s performance the authors used
the mean absolute error (MAE) in years and the e-
error (Escalera et al., 2015) for datasets where there
is no ground-truth. In this study, we evaluate the
MAE values for the real and the apparent age
The DAN method consists of a Convolutional
Neural Network for image alignment proposed by
(Kowalski, Naruniec and Trzcinski, 2017). The
proposal is inspired by the Cascade Shape
Regression (CSR) (Xiong and De La Torre, 2013)
framework, which consists of a combination of a
sequence of regressors to approximate nonlinear
mapping between the initial shape of the face and
the desired frontal face (Xiong and De La Torre,
2013). In the DAN algorithm, those regressors are
implemented using deep neural networks. The
authors used the Mean Error, as well as the Failure
IMPROVE 2021 - International Conference on Image Processing and Vision Engineering
Rate as metrics to support their results, so for this
study, we evaluate its performance using both
2.2 Datasets
As stated before, for each algorithm, a corresponding
set of images was selected according to their task.
Additionally, the chosen datasets had previously been
used to validate the algorithms, as is exposed in
(Schroff and Philbin, 2015), (Clapes et al., 2018), and
(Kowalski, Naruniec and Trzcinski, 2017).
The Labelled Faces in the Wild (LFW) (Huang et
al., 2007) was employed to evaluate the performance
of the FaceNet algorithm. The LFW dataset is
composed of 13233 face images corresponding to
5749 individuals. All images were extracted from the
internet, available as 250x250 pixel JPEG images,
most of them in color. The images are the result of the
Viola-Jones (Viola and Jones, 2001) face detection
algorithm and have been rescaled and cropped to the
aforementioned size. The dataset comprehends a
variety of scenarios in head pose, lighting, focus,
resolution, facial expression, age, gender, race,
accessories, make-up, occlusions, background, and
photographic quality.
To evaluate the performance of the DEX
algorithm, the Real and Apparent Age (APPA-
REAL) dataset (Clapes et al., 2018) was used. The
dataset contains 7591 images of 7000 individuals
with ages ranging from 0 to 91 years, in
unconstrained environments, and with varying
resolutions. The APPA-REAL allows testing age
estimation algorithms in both real and apparent age.
For the study, the validation set containing 1978
images was used.
Lastly, the challenging subset of the 300W dataset
was used to assess the performance of the DAN
method. This subset is called IBUG (Sagonas et al.,
2013) and consists of 135 images obtained from the
Internet, with variations in pose, expression,
illumination condition, and resolution. The dataset
provides landmark annotations for face alignment,
obtained employing the Multi-PIE annotation scheme
(Gross et al., 2010).
2.3 Distortions
To illustrate the effects of image quality in face
processing algorithms, four different distortions were
contemplated: noise, blur, brightness, and JPEG.
Noise can be caused by low-quality camera
sensors, or by the environmental conditions at the
moment of the acquisition (Mehmood and Selwal,
2020). For this study, we modeled the noise as a
Gaussian distribution with 0 mean and variance
ranging from 0.01 to 0.1 in steps of 0.01.
Blur can result from unfocused camera lenses or
moving targets (Huang et al., 2019). Additionally,
blurred images can simulate low-resolution images
due to the lack of details. For this study, we simulated
both motion blur and Gaussian blur. The motion blur
was achieved by filtering the images with different
sized kernels with value 1/(kernel size), and the
Gaussian noise effect was achieved by varying the
kernel’s standard deviation from 1 to 9 in steps of 1.
One way to simulate low and high illumination
conditions is through brightness. In that sense, we
simulated 10 stages for both high and low brightness
by altering the brightness factor using the Pillow
library for Python. For low brightness we altered the
brightness factor from 1 to 0, in steps of 0.1; and for
high brightness, the established range was 1.2-3.0,
with steps of 0.2.
JPEG compression is often cited as a distortion to
study due to its intrinsic characteristics, meaning, it is
a type of compression that provokes loss in the final
result. As was stated in the study carried on by
(Dodge and Karam, 2016), it is interesting to analyze
if the algorithms are affected by the quality of the
compression and in what measure it is relevant. To
evaluate the influence of JPEG compression in the
performance of the algorithms, the Pillow library was
used to obtain 10 levels of quality ranging from 5 to
95 in steps of 10.
To comprehend the results obtained from the
experiments, it is important to understand their
methodology. The DEX and DAN algorithms are
focused on one task each, so the experiments
consisted of evaluating their performance on their
specific task, through the selected metrics, and under
images with different distortions at different
magnitudes. However, FaceNet is a more complex
system designed to generate embedding for face
recognition tasks such as face identification and face
verification. Face identification consists of assigning
an identity to a face through a one-to-many operation,
where the embeddings of the unknown face are
compared with the ones in the dataset in order to
output the corresponding identity. Face verification,
on the other hand, is a one-to-one operation, where
the task is to check if the person’s embeddings are
close enough to the embeddings of the identity he or
she claims to be.
Understanding the Impact of Image Quality in Face Processing Algorithms
To evaluate the FaceNet performance, the
experiments followed the same methodology
proposed by (Huang et al., 2007), where the system
has to classify a pair of images as belonging to the
same person or different ones, according to
previously established pairs of matched and
mismatched persons from the dataset. In other
words, the experiments will be evaluating the
algorithm’s performance in a verification-like
The website for the LFW (LFW Face Database:
Main, 2018) dataset states that it is very difficult to
extrapolate from performance in verification to
performance in 1:N recognition”, although, given the
nature of these two tasks, it is safe to assume that any
changes in the algorithm performance during
verification, will be more noticeable during
The results obtained with each experiment are
shown in tables 1 to 6. The values in the first rows
correspond to the algorithms’ performance with the
original images, which was considered as the
reference for comparison.
3.1 Noise
Table 1 shows the behavior of all metrics across the
different levels of Gaussian noise. A significant
decrease in performance can be observed in all three
For the case of FaceNet, both metrics were
affected, however, there is a noticeable difference
between the accuracy of the model and the validation
rate when the FAR is set to 0.001. Even at the lowest
variance levels, the validation rate suffers
considerably more compared to the accuracy. The
algorithm appears to be robust in terms of accuracy,
however, as was stated before, a bigger impact could
be seen in the identification task.
The results obtained with the DEX algorithm
show the mean absolute error significantly increasing
in both classifications. In both cases, a 100% drop in
performance was quickly reached, as the values
doubled rapidly. On the other hand, after variance =
0.06, the errors plateaued.
Similar to the previous algorithms, DAN’s
performance worsens under the presence of noise.
Both metrics were greatly impacted even at the lower
variance values, however, the failure rate was
significantly more affected than the mean error.
3.2 Blur
3.2.1 Gaussian Blur
Table 2 shows the results obtained with images with
Gaussian blur. Like the previous experiment, all the
metrics were severely affected.
The results obtained with FaceNet show a bigger
decrease in performance than in the previous
experiment. A significant decline in both accuracy
and validation rate is observed after a standard
deviation of 3.0, where up to that point the accuracy
stayed above 0.97, and the validation rate was
approximately 0.85, however, from that on, both
metrics started decreasing at a higher rate.
The MAE values for the apparent and the real
age classification with the DEX algorithm are also
shown in Table 2. It is interesting to observe a slight
improvement in both metrics at the lowest levels of
gaussian blur. Since blurring techniques are used for
denoising, might be the case that some of the images
in the dataset were noisy, and the smoothness caused
by those levels of blur helped achieve better results.
From that point on, both metrics worsen
Similar to the noise experiments, the DAN’s
performance worsens under the presence of gaussian
blur. However, an interesting phenomenon occurred
where the mean error was more affected by Gaussian
blur than by noise, but the failure rate, although poor
in performance, achieved better results during this
experiment than the one before.
3.2.2 Motion Blur
The results obtained with the motion blur experiment
are shown in Table 3. Contrary to the behavior
observed with noise and blur with Gaussian
distributions, motion blur impacted significantly less
than the previous distortions.
The overall accuracy in the FaceNet algorithm
stayed almost constant across all kernel sizes, slightly
decreasing towards the bigger ones. The validation
rate at FAR = 0.001 suffered more than the accuracy,
however, its minimum value was considerably higher
than the values obtained in the previous experiments.
Motion blur also had a lesser impact on the DEX
algorithm than the previous distortions. Table 3
shows a slight improvement in both metrics under the
smaller kernels, as was the case with gaussian blur.
After that, both metrics worsen as the kernel size
A smaller impact on performance was observed in
the DAN algorithm as well. Both metrics increase as
IMPROVE 2021 - International Conference on Image Processing and Vision Engineering
the kernels get bigger, however, their behavior differs
from each other as the failure rate increases at a higher
rate than the mean error.
3.3 Brightness
3.3.1 Low Brightness
Low brightnesss effect is shown in Table 4. The
FaceNet and the DAN algorithms proved to be robust
when dealing with this type of image. Their metrics
display little variation for most of the brightness
factors, changing only with severely degraded
images, which correspond to images with little to no
For the case of the DEX algorithm, although less
affected than in previous experiments, a more
noticeable decrease in performance was observed
when compared with the other algorithms.
3.3.2 High Brightness
The performance achieved with high brightness
images is shown in Table 5. The results indicate that
excess brightness has a slightly bigger impact than the
opposite situation. All algorithms experienced a
greater drop in performance at the lower and medium
levels of brightness degradation during this
experiment than during the previous one. However,
the overall impact of high brightness is still relatively
small, especially when compared with Gaussian blur
and Gaussian noise.
3.4 JPEG Compression
The last distortion analyzed was the JPEG
compression. In this case, the goal was to observed
the effect of different qualities of compression in the
performance of the algorithms. Table 6 shows that the
three algorithms are robust under different
compression qualities. The only noticeable impact
occurred, in all three of them, at the lowest quality
The results obtained with the experiments show that
even though the distortions did not affect the
algorithms’ performance in the same measure,
patterns can be observed. In that sense, a series of
remarks can be outlined regarding the impact of each
distortion in these algorithms.
First, noise and blur, in their Gaussian
distribution, constitute the bigger threats to face
processing performance in terms of image quality.
Both distortions noticeably impacted the
algorithms’ metrics even at the lowest levels of
Second, even though Gaussian blur severely
impacted the performance of all three algorithms,
motion blur didn’t have the same effect. The results
show significantly less influence throughout the
majority of kernel sizes. This is an interesting result
because it indicates that not all blur constitutes a
threat to performance, unfocused images and lack of
detail have a bigger impact on performance than
Third, brightness and JPEG compression seem to
have a small impact on performance. According to the
graphs, noticeable impact is perceived only when the
images are severely degraded.
The focus of this paper was to study the behavior of
three different face processing algorithms under the
presence of noise, blur, brightness, and JPEG
compression, at different magnitudes. The goal was
to draw conclusions about the impact of these
distortions on face processing algorithms and obtain
a more insightful understanding of the influence of
image quality in these types of algorithms.
Based on the results, a series of remarks were
summarized in the previous section. From that, we
can conclude that the analyzed algorithms, and
potentially others, are unsuited for unconstrained
environments where noise and blur resembling
Gaussian distributions might be present. On the
positive side, their deployment in scenarios with
different conditions of JPEG compression and
brightness, would not be as compromised unless the
images are severely distorted.
The main contribution of this work is providing a
comprehensive study about the impact of several
image distortions in face processing algorithms.
Where most studies focused on one task, ours
comprehended several ones within the face
processing domain, which allowed us to extract
common patterns that arise when dealing with low-
quality images.
The information presented in this paper is useful
to develop adequate solutions for face image quality
assessment methods, oriented to improve face
processing performance with images of different
qualities. In that sense, our future work will be
Understanding the Impact of Image Quality in Face Processing Algorithms
focused on identifying the type and degree of the
distortions present in face images. We believe that
having that information beforehand, in conjunction
with the results presented in this paper, would lead to
the development of more robust face processing
Belhumeur, P. N., Hespanha, J. P. and Kriegman, D. J.
(1997) ‘Eigenfaces vs. Fisherfaces: Recognition
Using Class Specific Linear Projection’, IEEE
Transactions on Pattern Analysis and Machine
Intelligence, 19(7), pp. 711–720.
Chen, L. et al. (2009) ‘Face recognition with statistical
local binary patterns’, Proceedings of the 2009
International Conference on Machine Learning and
Cybernetics, 4(February), pp. 2433–2439. doi:
Clapes, A. et al. (2018)From apparent to real age:
Gender, age, ethnic, makeup, and expression bias
analysis in real age estimation’, IEEE Computer
Society Conference on Computer Vision and Pattern
Recognition Workshops, 2018–June, pp. 2436–2445.
doi: 10.1109/CVPRW.2018.00314.
Dodge, S. and Karam, L. (2016) ‘Understanding how
image quality affects deep neural networks’, in 2016
8th International Conference on Quality of
Multimedia Experience, QoMEX 2016. Institute of
Electrical and Electronics Engineers Inc., pp. 1–6. doi:
Escalera, S. et al. (2015) ‘ChaLearn Looking at People
2015: Apparent Age and Cultural Event Recognition
datasets and results’, in 2015 IEEE International
Conference on Computer Vision Workshop (ICCVW).
Gross, R. et al. (2010) ‘Multi-PIE’, in Proc Int Conf
Autom Face Gesture Recognit, pp. 807–813. doi:
Huang, G. B. et al. (2007) ‘Labeled Faces in the Wild: A
Database for Studying Face Recognition in
Unconstrained Environments’, Tech Report.
Huang, R. et al. (2019) ‘Image Blur Classification and
Unintentional Blur Removal’, IEEE Access. Institute
of Electrical and Electronics Engineers Inc., 7, pp.
106327–106335. doi:
Jaturawat, P. and Phankokkruad, M. (2017) ‘An
evaluation of face recognition algorithms and
accuracy based on video in unconstrained factors,
Proceedings - 6th IEEE International Conference on
Control System, Computing and Engineering,
ICCSCE 2016, (November), pp. 240–244. doi:
Kang, J. S. et al. (2018) ‘Age estimation robust to optical
and motion blurring by deep residual CNN’,
Symmetry, 10(4). doi: 10.3390/sym10040108.
Kowalski, M., Naruniec, J. and Trzcinski, T. (2017) ‘Deep
Alignment Network: A Convolutional Neural
Network for Robust Face Alignment’, in 2017 IEEE
Computer Society Conference on Computer Vision
and Pattern Recognition Workshops, pp. 2034–2043.
doi: 10.1109/CVPRW.2017.254.
LFW Face Database: Main (2018). Available at:
http://vis-www.cs.umass.edu/lfw/ (Accessed: 25
January 2021).
Li, P. et al. (2019) ‘Face Recognition in Low Quality
Images: A Survey’,
ACM Comput. Surv, 1(April-).
doi: 10.1145/nnnnnnn.nnnnnnn.
Mahmood, A. et al. (2019) ‘Recognition of Facial
Expressions under Varying Conditions Using Dual-
Feature Fusion’, Hindawi: Mathematical Problems in
Engineering, 2019, pp. 1–13. doi:
Mehmood, R. and Selwal, A. (2020) ‘A Comprehensive
Review on Face Recognition Methods and Factors
Affecting Facial Recognition Accuracy’, Lecture
Notes in Electrical Engineering, 597(January), pp.
455–467. doi: 10.1007/978-3-030-29407-6.
Rothe, R., Timofte, R. and Van Gool, L. (2018)Deep
Expectation of Real and Apparent Age from a Single
Image Without Facial Landmarks’, International
Journal of Computer Vision. Springer US, 126(24),
pp. 144–157. doi: 10.1007/s11263-016-0940-3.
Sagonas, C. et al. (2013) ‘300 Faces in-the-Wild
Challenge: The first facial landmark localization
Challenge’, in 2013 IEEE International Conference
on Computer Vision Workshops.
Schroff, F. and Philbin, J. (2015) ‘FaceNet: A Unified
Embedding for Face Recognition and Clustering’, in
2015 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR). Boston, MA, pp. 815–
823. doi: 10.1109/CVPR.2015.7298682.
Szegedy, C. et al. (2015) ‘Going deeper with
convolutions’, in 2015 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR). Boston, MA,
pp. 1–9.
Turk, M. A. and Pentland, A. P. (1991) ‘Face Recognition
Using Eigenfaces’, in Proceedings. 1991 IEEE
Computer Society Conference on Computer Vision
and Pattern Recognition, pp. 586–591. doi:
Viola, P. and Jones, M. (2001) Rapid Object Detection
using a Boosted Cascade of Simple Features’, in IEEE
Conference on Computer Vision and Pattern
Xiong, X. and De La Torre, F. (2013) ‘Supervised Descent
Method and its Applications to Face Alignment’, in
2013 IEEE Conference on Computer Vision and
Pattern Recognition.
Zeiler, M. D. and Fergus, R. (2014) ‘Visualizing and
Understanding Convolutional Networks’, in 13th
European Conference on Computer Vision – ECCV
IMPROVE 2021 - International Conference on Image Processing and Vision Engineering
Table 1: Gaussian noise experiment results.
Accuracy Validation
MAE Apparent
Age (Years)
MAE Real
Age (Years)
Mean Error Failure Rate
0,00 0,9965 0,98567 6,46788 7,6086 0,052 0,0518
0,989 0,926 11,835 12,659 0,075 0,237
0,977 0,853 13,219 14,062 0,108 0,415
0,965 0,751 14,087 14,940 0,134 0,556
0,948 0,618 14,511 15,361 0,164 0,659
0,924 0,393 14,757 15,551 0,197 0,763
0,897 0,330 14,958 15,778 0,233 0,844
0,876 0,183 15,096 15,913 0,255 0,867
0,845 0,109 15,112 15,948 0,289 0,911
0,825 0,052 15,196 16,012 0,312 0,963
0,782 0,048 15,129 15,956 0,342 0,985
Table 2: Gaussian blur experiment results.
Accuracy Validation
MAE Apparent
Age (Years)
MAE Real
Age (Years)
Mean Error Failure Rate
0 0,9965 0,9857 6,4679 7,6086 0,0524 0,0519
0,9957 0,9747 6,3348 7,5249 0,0553 0,1111
0,9899 0,9348 7,7535 8,6961 0,0866 0,2222
0,9758 0,8490 8,7475 10,0413 0,1508 0,4148
0,9587 0,6483 9,7207 11,0002 0,2484 0,5138
0,9192 0,4717 10,5318 11,8231 0,3410 0,5630
0,8515 0,2130 11,1935 12,4259 0,4268 0,6296
0,7840 0,0920 11,7378 12,9049 0,4960 0,7407
0,7388 0,0610 12,2777 13,3167 0,5495 0,7926
0,7055 0,0437 12,6175 13,6724 0,5942 0,8222
0,6792 0,0390 12,9041 13,9599 0,6312 0,8596
Table 3: Motion blur experiment results.
Kernel Size Accuracy Validation
MAE Apparent
Age (Years)
MAE Real
Age (Years)
Mean Error Failure Rate
0 0,9965 0,9857 6,4679 7,6086 0,0524 0,0519
0,9953 0,9853 6,2926 7,4780 0,0527 0,0593
0,9942 0,9767 6,3769 7,6107 0,0548 0,0889
0,9927 0,9650 6,6799 7,9063 0,0624 0,1556
0,9925 0,9417 7,0470 8,2531 0,0730 0,2296
0,9890 0,9240 7,4316 8,6433 0,0875 0,2963
0,9852 0,9043 7,7827 9,0208 0,1028 0,3556
0,9810 0,8623 8,1101 9,3635 0,1207 0,3926
0,9748 0,7930 8,4178 9,6787 0,1399 0,4444
0,9663 0,7397 8,7187 9,9900 0,1584 0,5037
0,9583 0,6803 8,9849 10,2712 0,1546 0,5333
Understanding the Impact of Image Quality in Face Processing Algorithms
Table 4: Low brightness experiment results.
Accuracy Validation
MAE Apparent
Age (Years)
MAE Real
Age (Years)
Mean Error Failure Rate
1,0 0,9965 0,9857 6,4679 7,6086 0,0524 0,0519
0,9963 0,9850 6,7060 7,8234 0,0525 0,0519
0,9965 0,9850 6,8995 8,0156 0,0528 0,0667
0,9963 0,9830 7,1050 8,2215 0,0529 0,0667
0,9962 0,9760 7,4271 8,5286 0,0534 0,0667
0,9960 0,9753 7,6763 8,8173 0,0543 0,0963
0,9953 0,9683 8,1112 9,2502 0,0558 0,0963
0,9935 0,9610 8,7266 9,8644 0,0627 0,1407
0,9857 0,9267 9,6779 10,8681 0,0878 0,2815
0,5475 0,0003 12,3576 13,4337 0,2446 0,7556
Table 5: High brightness experiment results.
Accuracy Validation
MAE Apparent
Age (Years)
MAE Real
Age (Years)
Mean Error Failure Rate
1,0 0,9965 0,9857 6,4679 7,6086 0,0524 0,0519
0,9952 0,9863 6,4763 7,6331 0,0524 0,0519
0,9942 0,9767 6,8012 8,0657 0,0532 0,0667
0,9920 0,9480 7,4323 8,6951 0,0547 0,0741
0,9822 0,8830 8,1965 9,4396 0,0591 0,0963
0,9695 0,7690 9,0058 10,2465 0,0623 0,1111
0,9540 0,6890 9,7599 11,0076 0,0651 0,1333
0,9318 0,5743 10,3806 11,6264 0,0687 0,1778
0,9085 0,4737 10,8840 12,0786 0,0729 0,1926
0,8882 0,3850 11,2700 12,4501 0,0764 0,2148
0,8618 0,3043 11,6299 12,7698 0,0819 0,2667
Table 6: JPEG compression experiment results.
Accuracy Validation
MAE Apparent
Age (Years)
MAE Real
Age (Years)
Mean Error Failure Rate
0 0,9965 0,9857 6,4679 7,6086 0,0524 0,0519
0,9958 0,9857 6,4862 7,6254 0,0524 0,0519
0,9962 0,9850 6,6462 7,7662 0,0525 0,0519
0,9957 0,9843 6,4717 7,6117 0,0527 0,0519
0,9960 0,9873 6,8818 7,9820 0,0528 0,0519
0,9957 0,9837 7,3550 8,4265 0,0530 0,0593
0,9955 0,9837 6,5798 7,7979 0,0530 0,0741
0,9958 0,9793 6,6900 7,8536 0,0536 0,0667
0,9955 0,9830 7,3014 8,4085 0,0535 0,0593
0,9932 0,9670 7,4256 8,6271 0,0552 0,0741
0,9507 0,5370 10,226 11,371 0,0848 0,2889
IMPROVE 2021 - International Conference on Image Processing and Vision Engineering