Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Multispectral Palmprint Recognition Using Textural Features

In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features and used them for palmprint recognition. Co-occurrence matrix can be used for textural feature extraction. As classifiers, we have used the minimum distance classifier (MDC) and the weighted majority voting system (WMV). The proposed method is tested on a well-known multispectral palmprint dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most scenarios which outperforms all previous works in multispectral palmprint recognition.Comment: 5 pages, Published in IEEE Signal Processing in Medicine and Biology Symposium 201

Characterization of palmprints by wavelet signatures via directional context modeling

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A Review on Palm Print Recognition System

Biometrics based authentication and recognition system helps to identify individuals based on various behavior and physical characteristics, which can be used for their unique personal identifications. Various physical characteristics like iris patterns, facial features, fingerprint patterns, retina patterns, palmprint patterns etc. are utilized for such identification purposes, Palm print recognition is counted as most suitable and reliable biometric recognition system because of its merits, such as user friendliness, low cost, high accuracy and high speed. A system that uses palmprint as recognize individuals involves the matching of the various principal lines, creases and wrinkles on the palm surface. Since the random orientations of muscles and tissues of the hand create the palmprint patterns during birth, these patterns are unique so no two palmprint patterns are exactly same for any individuals. This paper provides a detailed overview of palmprint recognition approaches, by describing the various steps and processing involve in palmprint identification

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Palmprint identification using restricted fusion

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe