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

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Palmprint Recognition Using Different Level of Information Fusion

The aim of this paper is to investigate a fusion approach suitable for palmprint recognition. Several number of fusion stageis analyse such as feature, matching and decision level. Fusion at feature level is able to increase discrimination power in the feature space by producing high dimensional fuse feature vector. Fusion at matching score level utilizes the matching output from different classifier to form a single value for decision process. Fusion at decision level on the other hand utilizes minimal information from a different matching process and the integration at this stage is less complex compare to other approach. The analysis shows integration at feature level produce the best recognition rates compare to the other method

Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters

Among several palmprint recognition methods proposed recently, coding-based approaches using multi-spectral palmprint images are attractive owing to their high recognition rates. Aiming to further improve the performance of these approaches, this paper presents a novel multi-spectral palmprint recognition approach based on oriented multiscale log-Gabor filters. The proposed method aims to enhance the recognition performances by proposing novel solutions at three stages of the recognition process. Inspired by the bitwise competitive coding, the feature extraction employs a multi-resolution log-Gabor filtering where the final feature map is composed of the winning codes of the lowest filters’ bank response. The matching process employs a bitwise Hamming distance and Kullback–Leibler divergence as novel metrics to enable an efficient capture of the intra- and inter-similarities between palmprint feature maps. Finally, the decision stage is carried pout using a fusion of the scores generated from different spectral bands to reduce overlapping. In addition, a fusion of the feature maps through two proposed novel feature fusion techniques to allow us to eliminate the inherent redundancy of the features of neighboring spectral bands is also proposed. The experimental results obtained using the multi-spectral palmprint database MS-PolyU have shown that the proposed method achieves high accuracy in mono-spectral and multi-spectral recognition performances for both verification and identification modes; and also outperforms the state-of-the-art methods

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and Securit

Improvements of local directional pattern for texture classification.

Doctoral Degree. University of KwaZulu-Natal, Durban.The Local Directional Pattern (LDP) method has established its effectiveness and performance compared to the popular Local Binary Pattern (LBP) method in different applications. In this thesis, several extensions and modification of LDP are proposed with an objective to increase its robustness and discriminative power. Local Directional Pattern (LDP) is dependent on the empirical choice of three for the number of significant bits used to code the responses of the Kirsch Mask operation. In a first study, we applied LDP on informal settlements using various values for the number of significant bits k. It was observed that the change of the value of the number of significant bits led to a change in the performance, depending on the application. Local Directional Pattern (LDP) is based on the computation Kirsch Mask application response values in eight directions. But this method ignores the gray value of the center pixel, which may lead to loss of significant information. Centered Local Directional Pattern (CLDP) is introduced to solve this issue, using the value of the center pixel based on its relations with neighboring pixels. Local Directional Pattern (LDP) also generates a code based on the absolute value of the edge response value; however, the sign of the original value indicates two different trends (positive or negative) of the gradient. To capture the gradient trend, Signed Local Directional Pattern (SLDP) and Centered-SLDP (C-SLDP) are proposed, which compute the eight edge responses based on the two different directions (positive or negative) of the gradients.The Directional Local Binary pattern (DLBP) is introduced, which adopts directional information to represent texture images. This method is more stable than both LDP and LBP because it utilizes the center pixel as a threshold for the edge response of a pixel in eight directions, instead of employing the center pixel as the threshold for pixel intensity of the neighbors, as in the LBP method. Angled Local directional pattern (ALDP) is also presented, with an objective to resolve two problems in the LDP method. These are the value of the number of significant bits k, and to taking into account the center pixel value. It computes the angle values for the edge response of a pixel in eight directions for each angle (0◦,45◦,90◦,135◦). Each angle vector contains three values. The central value in each vector is chosen as a threshold for the other two neighboring pixels. Circular Local Directional Pattern (CILDP) isalso presented, with an objective of a better analysis, especially with textures with a different scale. The method is built around the circular shape to compute the directional edge vector using different radiuses. The performances of LDP, LBP, CLDP, SLDP, C-SLDP, DLBP, ALDP and CILDP are evaluated using five classifiers (K-nearest neighbour algorithm (k-NN), Support Vector Machine (SVM), Perceptron, Naive-Bayes (NB), and Decision Tree (DT)) applied to two different texture datasets: Kylberg dataset and KTH-TIPS2-b dataset. The experimental results demonstrated that the proposed methods outperform both LDP and LBP