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Estimation Automatique de L’âge des Patients à Partir des Images Faciales
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La technique de la biométrie est une mesure des certains caractéristiques pour l'identification ou l’authentification d'un individu. Cette technique est utilisée de plus en plus aujourd'hui pour établir la reconnaissance de la personne dans un grand nombre d’applications diverses. Bien que les techniques de reconnaissance biométrique promettent d’être très performantes. Le vieillissement du visage a un impact négatif sur les performances de reconnaissance et de vérification et d’authentification du visage. Dans notre travail, nous avons présenté une analyse approfondie pour l'estimation automatique de l'âge des visages. Nous avons discuté les différents opérateurs d’extraction des caractéristiques utilisés dans l’estimation de l'âge.
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Due to advances in technology, today's biometric systems become vulnerable to spoof attacks made by fake faces. These attacks occur when an intruder attempts to fool an established face-based recognition system by presenting a fake face (e.g., print photo or replay attacks) in front of the camera instead of the intruder's genuine face. For this purpose, face antispoofing has become a hot topic in face analysis literature, where several applications with antispoofing task have emerged recently. We propose a solution for distinguishing between real faces and fake ones. Our approach is based on extracting features from the difference between successive frames instead of individual frames. We also used a multilevel representation that divides the frame difference into multiple multiblocks. Different texture descriptors (local binary patterns, local phase quantization, and binarized statistical image features) have then been applied to each block. After the feature extraction step, a Fisher score is applied to sort the features in ascending order according to the associated weights. Finally, a support vector machine is used to differentiate between real and fake faces. We tested our approach on three publicly available databases: CASIA Face Antispoofing database, Replay-Attack database, and MSU Mobile Face Spoofing database. The proposed approach outperforms the other state-of-the-art methods in different media and quality metrics.
The vulnerabilities of face-based biometric systems to presentation attacks have been finally recognized but yet we lack generalized software-based face presentation attack detection (PAD) methods performing robustly in practical mobile authentication scenarios. This is mainly due to the fact that the existing public face PAD datasets are beginning to cover a variety of attack scenarios and acquisition conditions but their standard evaluation protocols do not encourage researchers to assess the generalization capabilities of their methods across these variations. In this present work, we introduce a new public face PAD database, OULU-NPU, aiming at evaluating the generalization of PAD methods in more realistic mobile authentication scenarios across three covariates: unknown environmental conditions (namely illumination and background scene), acquisition devices and presentation attack instruments (PAI). This publicly available database consists of 5940 videos corresponding to 55 subjects recorded in three different environments using high-resolution frontal cameras of six different smartphones. The high-quality print and videoreplay attacks were created using two different printers and two different display devices. Each of the four unambiguously defined evaluation protocols introduces at least one previously unseen condition to the test set, which enables a fair comparison on the generalization capabilities between new and existing approaches. The baseline results using color texture analysis based face PAD method demonstrate the challenging nature of the database.
The Face recognition applications are the used way of authentication identity verification of mobile payment.
This popularity of face recognition is easy to raise concerns about face spoof attacks; use photo or video of an authorized person’s face to access to facilities or services. We propose an efficient and more robust algorithm for face spoof detection based on a combination between MLLBP and MLBSIF. An ensemble classifier, consisting of Lib-SVM classifiers using different face spoof attacks (e.g., printed photo and replayed
video) to trained our model which is used to distinguish between genuine and spoof faces. We tested our approach on CASIA FASD database. Our proposed approach conduct a good result compared with the state of art.
Biometric recognition, or simply biometrics, is a rapidly evolving field with applications ranging from accessing one's computer, to gaining entry into a country. Biometric systems rely on the use of physical or behavioral traits, such as fingerprints, face, voice and hand geometry, to establish the identity of an individual. The deployment of large-scale biometric systems in both commercial (e.g., grocery stores, amusement parks, airports) and government (e.g., US-VISIT) applications, increases the public's awareness of this technology. This rapid growth also highlights the challenges associated with designing and deploying biometric systems. Indeed, the problem of biometric recognition is a grand challenge in its own right. The past five years have seen a significant growth in biometric research resulting in the development of innovative sensors, robust and efficient algorithms for feature extraction and matching, enhanced test methodologies and novel applications. These advances have resulted in robust, accurate, secure and cost effective biometric systems.
The Handbook of Biometrics -- an edited volume by prominent invited researchers in biometrics -- describes the fundamentals as well as the latest advancements in the burgeoning field of biometrics. It is designed for professionals, practitioners and researchers in biometrics, pattern recognition and computer security. The Handbook of Biometrics can be used as a primary textbook for an undergraduate biometrics class. This book is also suitable as a secondary textbook or reference for advanced-level students in computer science.
Human recognition has become an important topic as the need and investments for security applications grow continuously. Numerous biometric systems exist which utilize various human characteristics. Among all biometrics traits, face recognition is advantageous in terms of accessibility and reliability. In the thesis, two challenges in face recognition are analyzed. The first one is face spoofing. Spoofing in face recognition is explained together with the countermeasure techniques that are proposed for the protection of face recognition systems against spoofing attacks. For this purpose, both 2D photograph and 3D mask attacks are analyzed. The second challenge explored in the thesis is disguise variations, which are due to facial alterations, facial makeup and facial accessories (occlusions). The impact of these disguise variations on face recognition is explored, separately. Then, techniques which are robust against disguise variations are proposed.
Many trait-specific countermeasures to face spoofing attacks have been developed for security of face authentication. However, there is no superior face anti-spoofing technique to deal with every kind of spoofing attack in varying scenarios. In order to improve the generalization ability of face anti-spoofing approaches, an extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection. Shearlet is utilized to develop an image quality-based liveness feature. Dense optical flow is utilized to extract motion-based liveness features. A bottleneck feature fusion strategy can integrate different liveness features effectively. The proposed approach was evaluated on three public face anti-spoofing databases. A half total error rate (HTER) of 0% and an equal error rate (EER) of 0% were achieved on both REPLAY-ATTACK database and 3D-MAD database. An EER of 5.83% was achieved on CASIA-FASD database.
The need for automation of the identity recognition process for a vast number of applications
resulted in great advancement of biometric systems in the recent years. Yet, many studies
indicate that these systems suffer from vulnerabilities to spoofing (presentation) attacks: a
weakness that may compromise their usage in many cases. Face verification systems account
for one of the most attractive spoofing targets, due to the easy access to face images of users,
as well as the simplicity of the spoofing attack manufacturing process.
Many counter-measures to spoofing have been proposed in the literature. They are based on
different cues that are used to distinguish between real accesses and spoofing attacks. The
task of detecting spoofing attacks is most often considered as a binary classification problem,
with real accesses being the positive class and spoofing attacks being the negative class.
The main objective of this thesis is to put the problem of anti-spoofing in a wider context,
with an accent on its cooperation with a biometric verification system. In such a context, it
is important to adopt an integrated perspective on biometric verification and anti-spoofing.
In this thesis we identify and address three points where integration of the two systems is of
interest.
The first integration point is situated at input-level. At this point, we are concerned with
providing a unified information that both verification and anti-spoofing systems use. The
unified information includes the samples used to enroll clients in the system, as well as the
identity claims of the client at query time. We design two anti-spoofing schemes, one with
a generative and one with a discriminative approach, which we refer to as client-specific, as
opposed to the traditional client-independent ones.
The proposed methods are applied on several case studies for the face mode. Overall, the
experimental results prove the integration to be beneficial for creating trustworthy face verification
systems. At input-level, the results show the advantage of the client-specific approaches
over the client-independent ones. At output-level, they present a comparison of the fusion
methods. The case studies are furthermore used to demonstrate the EPS framework and its
potential in evaluation of biometric verification systems under spoofing attacks.
The source code for the full set of methods is available as free software, as a satellite package to
the free signal processing and machine learning toolbox Bob. It can be used to reproduce the
results of the face mode case studies presented in this thesis, as well as to perform additional
analysis and improve the proposed methods. Furthermore, it can be used to design case
studies applying the proposed methods to other biometric modes.
At the second integration point, situated at output-level, we address the issue of combining
the output of biometric verification and anti-spoofing systems in order to achieve an optimal
combined decision about an input sample. We adopt a multiple expert fusion approach and we
investigate several fusion methods, comparing the verification performance and robustness
to spoofing of the fused systems.
The third integration point is associated with the evaluation process. The integrated perspective
implies three types of inputs for the biometric system: real accesses, zero-effort impostors
and spoofing attacks. We propose an evaluation methodology for biometric verification systems
under spoofing attacks, called Expected Performance and Spoofability (EPS) framework,
which accounts for all the three types of input and the error rates associated with them. Within
this framework, we propose the EPS Curve (EPSC), which enables unbiased comparison of
systems.