Palmprint Recognition by using Bandlet, Ridgelet, Wavelet and Neural Network

Palmprint recognition has emerged as a substantial biometric based personal identification. Tow types of biometrics palmprint feature. high resolution feature that includes: minutia points, ridges and singular points that could be extracted for forensic applications. Moreover, low resolution feature such as wrinkles and principal lines which could be extracted for commercial applications. This paper uses 700nm spectral band PolyU hyperspectral palmprint database. Multiscale image transform: bandlet, ridgelet and 2D discrete wavelet have been applied to extract feature. The size of features are reduced by using principle component analysis and linear discriminate analysis. Feed-forward Back-propagation neural network is used as a classifier. The recognition rate accuracy shows that bandlet transform outperforms others

Curvelet Transform-Based Techniques For Biometric Person Identification

Biometric person identification refers to the recognition of a person based on the physical or behavioral traits. Palm print based biometric identification system is one of the low cost biometric systems, since the palm image can be obtained using low cost sensors, such as desktop scanners and web cameras. Because of ease of image acquisition of palm prints and identification accuracy, palm images are used in both uni- modal and multimodal biometric systems. A multi-scale and multi-directional representation is desirable to represent thick and scattered thin lines of a palm image. Multi-scale and multi-directional representation can also be used in image fusion, where two images of two different biometric traits can be fused to a single image to improve the identification accuracy. Face and palm images can be fused to keep the desired high pass information of the palm images and the low pass information of the face images. The Curvelet transform is a multi-scale and multi-directional geometric transform that provides a better representation of the objects with edges and requires a small number of curvelet coefficients to represent the curves. In this thesis, two methods using the very desirable characteristics of the curvelet transform are proposed for both the uni-modal and bi-modal biometric systems. A palm curvelet code (PCC) for palm print based uni-modal biometric systems and a pixel-level fusion method for face and palm based bi-modal biometric systems are developed. A simple binary coding technique that represents the structural information in curvelet directional sub-bands is used to obtain the PCC. Performance of the PCC is evaluated for both identification and verification modes of a palm print based biometric system, and then, the use of PCC in hierarchical identification is investigated. In the pixel-level fusion scheme for a bi-modal system, face and palm images are fused in the curvelet transform domain using mean-mean fusion rule. Extensive experimentations are carried out on three publicly available palm databases and one face database to evaluate the performance in terms of the commonly used metrics, and it is shown that the proposed methods provide a better performance compared to other existing methods

A new approach to face recognition using Curvelet Transform

Multiresolution tools have been profusely employed in face recognition. Wavelet Transform is the best known among these multiresolution tools and is widely used for identification of human faces. Of late, following the success of wavelets a number of new multiresolution tools have been developed. Curvelet Transform is a recent addition to that list. It has better directional ability and effective curved edge representation capability. These two properties make curvelet transform a powerful weapon for extracting edge information from facial images. Our work aims at exploring the possibilities of curvelet transform for feature extraction from human faces in order to introduce a new alternative approach towards face recognition

Palm Vein Verification Using Multiple Features and Locality Preserving Projections

Biometrics is defined as identifying people by their physiological characteristic, such as iris pattern, fingerprint, and face, or by some aspects of their behavior, such as voice, signature, and gesture. Considerable attention has been drawn on these issues during the last several decades. And many biometric systems for commercial applications have been successfully developed. Recently, the vein pattern biometric becomes increasingly attractive for its uniqueness, stability, and noninvasiveness. A vein pattern is the physical distribution structure of the blood vessels underneath a person’s skin. The palm vein pattern is very ganglion and it shows a huge number of vessels. The attitude of the palm vein vessels stays in the same location for the whole life and its pattern is definitely unique. In our work, the matching filter method is proposed for the palm vein image enhancement. New palm vein features extraction methods, global feature extracted based on wavelet coefficients and locality preserving projections (WLPP), and local feature based on local binary pattern variance and locality preserving projections (LBPV_LPP) have been proposed. Finally, the nearest neighbour matching method has been proposed that verified the test palm vein images. The experimental result shows that the EER to the proposed method is 0.1378%

Performance analysis of multimodal biometric fusion

Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. In fact in recent years biometric-based authentication techniques received more attention due to increased concerns in security. Most biometric systems that are currently in use typically employ a single biometric trait. Such systems are called unibiometric systems. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the challenges can be handled by designing a multimodal biometric system. Multimodal biometric systems are those which utilize or are capable of utilizing, more than one physiological or behavioural characteristic for enrolment, verification, or identification. In this thesis, we propose a novel fusion approach at a hybrid level between iris and online signature traits. Online signature and iris authentication techniques have been employed in a range of biometric applications. Besides improving the accuracy, the fusion of both of the biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. In this doctoral dissertation, we make a first attempt to combine online signature and iris biometrics. We principally explore the fusion of iris and online signature biometrics and their potential application as biometric identifiers. To address this issue, investigations is carried out into the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. We compare the results of the multimodal approach with the results of the individual online signature and iris authentication approaches. This dissertation describes research into the feature and decision fusion levels in multimodal biometrics.State of Kuwait – The Public Authority of Applied Education and Trainin

Complex-Wavelet Structural Similarity Based Image Classification

Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in image classification problems has not been deeply investi- gated. In this study, we introduce a series of novel image classification algorithms based on CW-SSIM and use handwritten digit and face image recognition as examples for demonstration, including CW-SSIM based nearest neighbor method, CW-SSIM based k means method, CW-SSIM based support vector machine method (SVM) and CW-SSIM based SVM using affinity propagation. Among the proposed approaches, the best compromise between accuracy and complexity is obtained by the CW-SSIM support vector machine algorithm, which combines an unsupervised clustering method to divide the training images into clusters with representative images and a supervised learning method based on support vector machines to maximize the classification accuracy. Our experiments show that such a conceptually simple image classification method, which does not involve any registration, intensity normalization or sophisticated feature extraction processes, and does not rely on any modeling of the image patterns or distortion processes, achieves competitive performance with reduced computational cost

Curvelet and Ridgelet-based Multimodal Biometric Recognition System using Weighted Similarity Approach

Biometric security artifacts for establishing the identity of a person with high confidence have evoked enormous interest in security and access control applications for the past few years. Biometric systems based solely on unimodal biometrics often suffer from problems such as noise, intra-class variations and spoof attacks. This paper presents a novel multimodal biometric recognition system by integrating three biometric traits namely iris, fingerprint and face using weighted similarity approach. In this work, the multi-resolution features are extracted independently from query images using curvelet and ridgelet transforms, and are then compared to the enrolled templates stored in the database containing features of each biometric trait. The final decision is made by normalizing the feature vectors, assigning different weights to the modalities and fusing the computed scores using score combination techniques. This system is tested with the public unimodal databases such as CASIA–Iris-V3-Interval, FVC2004, ORL and self-built multimodal databases. Experimental results obtained shows that the designed system achieves an excellent recognition rate of 98.75 per cent and 100 per cent for the public and self-built databases respectively and provides ultra high security than unimodal biometric systems.Defence Science Journal, 2014, 64(2), pp. 106-114. DOI: http://dx.doi.org/10.14429/dsj.64.346