Palmprint identification using an ensemble of sparse representations

Among various palmprint identification methods proposed in the literature, sparse representation for classification (SRC) is very attractive offering high accuracy. Although SRC has good discriminative ability, its performance strongly depends on the quality of the training data. In particular, SRC suffers from two major problems: lack of training samples per class and large intra-class variations. In fact, palmprint images not only contain identity information but they also have other information, such as illumination and geometrical distortions due to the unconstrained conditions and the movement of the hand. In this case, the sparse representation assumption may not hold well in the original space since samples from different classes may be considered from the same class. This paper aims to enhance palmprint identification performance through SRC by proposing a simple yet efficient method based on an ensemble of sparse representations through an ensemble of discriminative dictionaries satisfying SRC assumption. The ensemble learning has the advantage to reduce the sensitivity due to the limited size of the training data and is performed based on random subspace sampling over 2D-PCA space while keeping the image inherent structure and information. In order to obtain discriminative dictionaries satisfying SRC assumption, a new space is learned by minimizing and maximizing the intra-class and inter-class variations using 2D-LDA. Extensive experiments are conducted on two publicly available palmprint data sets: multispectral and PolyU. Obtained results showed very promising results compared with both state-of-the-art holistic and coding methods. Besides these findings, we provide an empirical analysis of the parameters involved in the proposed technique to guide the neophyte. 2018 IEEE.This work was supported by the National Priority Research Program from the Qatar National Research Fund under Grant 6-249-1-053. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.Scopu

Masked face recognition using deep learning: a review

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR

A Novel Efficient Classwise Sparse and Collaborative Representation for Holistic Palmprint Recognition

Palmprint recognition is an important and widely used modality in biometric systems. It has a high reliability, stability and user acceptability. Although the discriminative ability of the existing state-of-the-art holistic techniques, their effectiveness heavily relies upon the quality of training data. Indeed, palmprint images contain different information including identity, illumination and distortions related to the acquisition systems. To overcome this problem, we explore a novel efficient holistic Classwise Sparse and Collaborative Representation (CSR). Extensive experiments have been performed on two existing and widely used palmprint datasets: multispectral and Poly U. The obtained experimental results demonstrated very encouraging performances when compared to state-of-the-art techniques. � 2018 IEEE.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 8-140-2-065. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.Scopu