Textural features for fingerprint liveness detection

The main topic ofmy research during these three years concerned biometrics and in particular the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. Several videos posted on YouTube show how to violate these devices by using fake fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a threat to the existing fingerprint recognition systems. Despite the fact that many algorithms have been proposed so far, none of them showed the ability to clearly discriminate between real and fake fingertips. In my work, after a study of the state-of-the-art I paid a special attention on the so called textural algorithms. I first used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms in the FLD field. In the last two years I worked especially on what we called the “user specific” problem. In the extracted features we noticed the presence of characteristic related not only to the liveness but also to the different users. We have been able to improve the obtained results identifying and removing, at least partially, this user specific characteristic. Since 2009 the Department of Electrical and Electronic Engineering of the University of Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity have organized the Fingerprint Liveness Detection Competition (LivDet). I have been involved in the organization of both second and third editions of the Fingerprint Liveness Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets

�rm Face image analysis in dynamic sce

Automatic personality analysis using computer vision is a relatively new research topic. It investigates how a machine could automatically identify or synthesize human personality. Utilizing time-based sequence information, numerous attempts have been made to tackle this problem. Various applications can benefit from such a system, including prescreening interviews and personalized agents. In this thesis, we address the challenge of estimating the Big-Five personality traits along with the job candidate screening variable from facial videos. We proposed a novel framework to assist in solving this challenge. This framework is based on two main components: (1) the use of Pyramid Multilevel (PML) to extract raw facial textures at different scales and levels; and (2) the extension of the Covariance Descriptor (COV) to combine several local texture features of the face image, such as Local Binary Patterns (LBP), Local Directional Pattern (LDP), Binarized Statistical Image Features (BSIF), and Local Phase Quantization (LPQ). The video stream features are then represented by merging the face feature vectors, where each face feature vector is formed by concatenating all iii iii the PML-COV feature blocks. These rich low-level feature blocks are obtained by feeding the textures of PML face parts into the COV descriptor. The state-of-the-art approaches are even hand-crafted or based on deep learning. The Deep Learning methods perform better than the hand-crafted descriptors, but they are computationally and experimentally expensive. In this study, we compared five hand-crafted methods against five methods based on deep learning in order to determine the optimal balance between accuracy and computational cost. The obtained results of our PML-COV framework on the ChaLearn LAP APA2016 dataset compared favourably with the state-ofthe-art approaches, including deep learning-based ones. Our future aim is to apply this framework to other similar computer vision problems

Recognizing Visual Object Using Machine Learning Techniques

Nowadays, Visual Object Recognition (VOR) has received growing interest from researchers and it has become a very active area of research due to its vital applications including handwriting recognition, diseases classification, face identification ..etc. However, extracting the relevant features that faithfully describe the image represents the challenge of most existing VOR systems. This thesis is mainly dedicated to the development of two VOR systems, which are presented in two different contributions. As a first contribution, we propose a novel generic feature-independent pyramid multilevel (GFIPML) model for extracting features from images. GFIPML addresses the shortcomings of two existing schemes namely multi-level (ML) and pyramid multi-level (PML), while also taking advantage of their pros. As its name indicates, the proposed model can be used by any kind of the large variety of existing features extraction methods. We applied GFIPML for the task of Arabic literal amount recognition. Indeed, this task is challenging due to the specific characteristics of Arabic handwriting. While most literary works have considered structural features that are sensitive to word deformations, we opt for using Local Phase Quantization (LPQ) and Binarized Statistical Image Feature (BSIF) as Arabic handwriting can be considered as texture. To further enhance the recognition yields, we considered a multimodal system based on the combination of LPQ with multiple BSIF descriptors, each one with a different filter size. As a second contribution, a novel simple yet effcient, and speedy TR-ICANet model for extracting features from unconstrained ear images is proposed. To get rid of unconstrained conditions (e.g., scale and pose variations), we suggested first normalizing all images using CNN. The normalized images are fed then to the TR-ICANet model, which uses ICA to learn filters. A binary hashing and block-wise histogramming are used then to compute the local features. At the final stage of TR-ICANet, we proposed to use an effective normalization method namely Tied Rank normalization in order to eliminate the disparity within blockwise feature vectors. Furthermore, to improve the identification performance of the proposed system, we proposed a softmax average fusing of CNN-based feature extraction approaches with our proposed TR-ICANet at the decision level using SVM classifier

Textural features for fingerprint liveness detection

The main topic ofmy research during these three years concerned biometrics and in particular the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. Several videos posted on YouTube show how to violate these devices by using fake fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a threat to the existing fingerprint recognition systems. Despite the fact that many algorithms have been proposed so far, none of them showed the ability to clearly discriminate between real and fake fingertips. In my work, after a study of the state-of-the-art I paid a special attention on the so called textural algorithms. I first used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms in the FLD field. In the last two years I worked especially on what we called the “user specific” problem. In the extracted features we noticed the presence of characteristic related not only to the liveness but also to the different users. We have been able to improve the obtained results identifying and removing, at least partially, this user specific characteristic. Since 2009 the Department of Electrical and Electronic Engineering of the University of Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity have organized the Fingerprint Liveness Detection Competition (LivDet). I have been involved in the organization of both second and third editions of the Fingerprint Liveness Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets

A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional neural network (CNN). The VGG-16 network uses different face viewpoints rendered from a full light field image, which are organised as a pseudo-video sequence. A comprehensive set of experiments has been conducted with the IST-EURECOM light field face database, for varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.Comment: Submitted to IEEE Transactions on Circuits and Systems for Video Technolog

Advancing iris biometric technology

PhD ThesisThe iris biometric is a well-established technology which is already in use in several nation-scale applications and it is still an active research area with several unsolved problems. This work focuses on three key problems in iris biometrics namely: segmentation, protection and cross-matching. Three novel methods in each of these areas are proposed and analyzed thoroughly. In terms of iris segmentation, a novel iris segmentation method is designed based on a fusion of an expanding and a shrinking active contour by integrating a new pressure force within the Gradient Vector Flow (GVF) active contour model. In addition, a new method for closed eye detection is proposed. The experimental results on the CASIA V4, MMU2, UBIRIS V1 and UBIRIS V2 databases show that the proposed method achieves state-of-theart results in terms of segmentation accuracy and recognition performance while being computationally more efficient. In this context, improvements by 60.5%, 42% and 48.7% are achieved in segmentation accuracy for the CASIA V4, MMU2 and UBIRIS V1 databases, respectively. For the UBIRIS V2 database, a superior time reduction is reported (85.7%) while maintaining a similar accuracy. Similarly, considerable time improvements by 63.8%, 56.6% and 29.3% are achieved for the CASIA V4, MMU2 and UBIRIS V1 databases, respectively. With respect to iris biometric protection, a novel security architecture is designed to protect the integrity of iris images and templates using watermarking and Visual Cryptography (VC). Firstly, for protecting the iris image, text which carries personal information is embedded in the middle band frequency region of the iris image using a novel watermarking algorithm that randomly interchanges multiple middle band pairs of the Discrete Cosine Transform (DCT). Secondly, for iris template protection, VC is utilized to protect the iii iris template. In addition, the integrity of the stored template in the biometric smart card is guaranteed by using the hash signatures. The proposed method has a minimal effect on the iris recognition performance of only 3.6% and 4.9% for the CASIA V4 and UBIRIS V1 databases, respectively. In addition, the VC scheme is designed to be readily applied to protect any biometric binary template without any degradation to the recognition performance with a complexity of only O(N). As for cross-spectral matching, a framework is designed which is capable of matching iris images in different lighting conditions. The first method is designed to work with registered iris images where the key idea is to synthesize the corresponding Near Infra-Red (NIR) images from the Visible Light (VL) images using an Artificial Neural Network (ANN) while the second method is capable of working with unregistered iris images based on integrating the Gabor filter with different photometric normalization models and descriptors along with decision level fusion to achieve the cross-spectral matching. A significant improvement by 79.3% in cross-spectral matching performance is attained for the UTIRIS database. As for the PolyU database, the proposed verification method achieved an improvement by 83.9% in terms of NIR vs Red channel matching which confirms the efficiency of the proposed method. In summary, the most important open issues in exploiting the iris biometric are presented and novel methods to address these problems are proposed. Hence, this work will help to establish a more robust iris recognition system due to the development of an accurate segmentation method working for iris images taken under both the VL and NIR. In addition, the proposed protection scheme paves the way for a secure iris images and templates storage. Moreover, the proposed framework for cross-spectral matching will help to employ the iris biometric in several security applications such as surveillance at-a-distance and automated watch-list identification.Ministry of Higher Education and Scientific Research in Ira

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Local quality-based matching of faces for watchlist screening applications

Video surveillance systems are often exploited by safety organizations for enhanced security and situational awareness. A key application in video surveillance is watchlist screening where target individuals are enrolled to a still-to-video Face Recognition (FR) system using single still images captured a priori under controlled conditions. Watchlist Screening is a very challenging application. Indeed, the latter must provide accurate decisions and timely recognition using limited number of reference faces for the system’s enrolment. This issue is often called the "Single Sample Per Person" (SSPP) problem. Added to that, uncontrolled factors such as variations in illumination pose and occlusion is unpreventable in real case video surveillance which causes the degradation of the FR system’s performance. Another major problem in such applications is the camera interoperability. This means that there is a huge gap between the camera used for taking the still images and the camera used for taking the video surveillance footage in terms of quality and resolution. This issue hinders the classification process then decreases the system‘s performance. Controlled and uniform lighting is indispensable for having good facial captures that contributes in the recognition performance of the system. However, in reality, facial captures are poor in illumination factor and are severely affecting the system’s performance. This is why it is important to implement a FR system which is invariant to illumination changes. The first part of this Thesis consists in investigating different illumination normalization (IN) techniques that are applied at the pre-processing level of the still-to-video FR. Afterwards IN techniques are compared to each other in order to pinpoint the most suitable technique for illumination invariance. In addition, patch-based methods for template matching extracts facial features from different regions which offers more discriminative information and deals with occlusion issues. Thus, local matching is applied for the still-to-video FR system. For that, a profound examination is needed on the manner of applying these IN techniques. Two different approaches were conducted: the global approach which consists in performing IN on the image then performs local matching and the local approach which consists in primarily dividing the images into non overlapping patches then perform on individually on each patch each IN technique. The results obtained after executing these experiments have shown that the Tan and Triggs (TT) and Multi ScaleWeberfaces are likely to offer better illumination invariance for the still-to-video FR system. In addition to that, these outperforming IN techniques applied locally on each patch have shown to improve the performance of the FR compared to the global approach. The performance of a FR system is good when the training data and the operation data are from the same distribution. Unfortunately, in still-to-video FR systems this is not satisfied. The training data are still, high quality, high resolution and frontal images. However, the testing data are video frames, low quality, low resolution and varying head pose images. Thus, the former and the latter do not have the same distribution. To address this domain shift, the second part of this Thesis consists in presenting a new technique of dynamic regional weighting exploiting unsupervised domain adaptation and contextual information based on quality. The main contribution consists in assigning dynamic weights that is specific to a camera domain.This study replaces the static and predefined manner of assigning weights. In order to assess the impact of applying local weights dynamically, results are compared to a baseline (no weights) and static weighting technique. This context based approach has proven to increase the system’s performance compared to the static weighting that is dependent on the dataset and the baseline technique which consists of having no weights. These experiments are conducted and validated using the ChokePoint Dataset. As for the performance of the still-to-video FR system, it is evaluated using performance measures, Receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve analysis