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

Palm Print Recognition Using Curve let Transform

In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Traditional security and identification are not sufficient enough; people need to find a new authentic system based on behavioral & physiological characteristics of person which is called as Biometric. Palm print recognition gives several advantages over the other biometrics such as low resolution, low cost, non-intrusiveness and stable structure features. Now a days Palm print based personal verification system is used in many security application due to its ease of acquisition, high user acceptance and reliability. Various approaches which deal with palm recognition are texture approach, line approach and appearance approach. By using texture approach it is possible to obtain texture sample with low resolution and texture is much more stable as compare to line and appearance. This paper is aimed to analyze the performance of palm print recognition systems using Curvelet features and for dimension reduction PCA is used

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

In this paper, the authors present a novel personal verification system based on the likelihood ratio test for fusion of match scores from multiple biometric matchers (face, fingerprint, hand shape, and palm print). In the proposed system, multimodal features are extracted by Zernike Moment (ZM). After matching, the match scores from multiple biometric matchers are fused based on the likelihood ratio test. A finite Gaussian mixture model (GMM) is used for estimating the genuine and impostor densities of match scores for personal verification. Our approach is also compared to some different famous approaches such as the support vector machine and the sum rule with min-max. The experimental results have confirmed that the proposed system can achieve excellent identification performance for its higher level in accuracy than different famous approaches and thus can be utilized for more application related to person verification

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

Fusion of geometric and texture features for finger knuckle surface recognition

AbstractHand-based biometrics plays a significant role in establishing security for real-time environments involving human interaction and is found to be more successful in terms of high speed and accuracy. This paper investigates on an integrated approach for personal authentication using Finger Back Knuckle Surface (FBKS) based on two methodologies viz., Angular Geometric Analysis based Feature Extraction Method (AGFEM) and Contourlet Transform based Feature Extraction Method (CTFEM). Based on these methods, this personal authentication system simultaneously extracts shape oriented feature information and textural pattern information of FBKS for authenticating an individual. Furthermore, the proposed geometric and textural analysis methods extract feature information from both proximal phalanx and distal phalanx knuckle regions (FBKS), while the existing works of the literature concentrate only on the features of proximal phalanx knuckle region. The finger joint region found nearer to the tip of the finger is called distal phalanx region of FBKS, which is a unique feature and has greater potentiality toward identification. Extensive experiments conducted using newly created database with 5400 FBKS images and the obtained results infer that the integration of shape oriented features with texture feature information yields excellent accuracy rate of 99.12% with lowest equal error rate of 1.04%

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