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Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.

Recently, 3D palmprint recognition systems have started to gain the attention of researchers compared to their 2D counterpart. The key task in the design of a biometric identification system is the choice of the method of extracting discriminating information from 2D/3D images. Given the enormous success of deep learning approaches in the field of feature extraction and classification, we propose in this paper the use of Transfer learning networks in order to build a palmprint based person identification system. Also, this paper aims to show the efficiency of feature selection to improve system performance. To do this, a Relief method was used to perform the feature selection task. Experiments on a 2D/3D palmprint database with 8000 samples show that the proposed scheme can significantly improve the efficiency of palmprint identification.

ISBN 978-1-7281-5836-5 ; e-ISBN 978-1-7281-5835-8; International audience; Biometric technology has become essential in our daily life. In such a biometric system, personal identification is based on behavioral or biological characteristics. Recently, the trait of the Finger-Knuckle-Print (FKP) is used due to its ease of use and low cost. In order to develop an efficient recognition system based on these images, we propose a deep learning method where we use our own Convolutional Neural Network (CNN) to identify persons. Excellent results were conducted with unimodal and multimodal identification systems.

International audience; 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.

International audience; Facial demographic classification is an attractive topic in computer vision. Attributes such as age and gender can be used in many real life application such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparaison. Our proposed approach is based on Multi Level Local Phase Quantization (ML-LPQ) features which are extracted from normalized face images. Two different Support Vector Machines (SVM) models are used to predict the age group and the gender of a person. The experimental results on the benchmark Image of Groups dataset showed the superiority of our approach compared to the state-of-the-art.

International audience; Todays biometric systems are vulnerable to spoofattacks made by non-real faces. The problem is when a personshows in front of camera a print photo or a picture from cellphone. We study in this paper an anti-spoofing solution fordistinguishing between ’live’ and ’fake’ faces. In our approachwe used overlapping block LBP operator to extract features ineach region of the image. To reduce the features we used Fisher-Score. Finally, we used a nonlinear Support Vector Machine(SVM) classifier with kernel function for determining whether theinput image corresponds to a live face or not. Our experimentalanalysis on a publicly available NUAA and CASIA face anti-spoofing databases following the standard protocols showed goodresults