An Efficient Vein Pattern-based Recognition System

This paper presents an efficient human recognition system based on vein pattern from the palma dorsa. A new absorption based technique has been proposed to collect good quality images with the help of a low cost camera and light source. The system automatically detects the region of interest from the image and does the necessary preprocessing to extract features. A Euclidean Distance based matching technique has been used for making the decision. It has been tested on a data set of 1750 image samples collected from 341 individuals. The accuracy of the verification system is found to be 99.26% with false rejection rate (FRR) of 0.03%.Comment: IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Vein Pattern Extraction Using Near Infrared Imaging for Biometric Purposes

Biomedical verification has been broadly examined for many years and pulled in much consideration because of its huge potential security application. Vein is less prone to damage and almost improbable to copy than any other physiological as well as behavioural biometric features such as fingerprint, iris, face and voice recognition. This paper proposes an efficient vein extraction method on low quality vein images taken by a camera absorbing near infrared light (NIR camera). At first, the image is contrast enhanced using contrast limited adaptive histogram equalization (CLAHE); secondly, local threshold method is applied on small blocks of the image followed by several morphological operations such as fill, erosion, dilation, clean and bridge, performed sequentially, for better accuracy. Experimental results obtained for extraction show that the proposed method can reap better results with reduced complexity. After extraction, matching of the test image with the template images stored in the database are matched using minutiae (point-to-point pattern). An orientation detector which filters out missing or unnecessary or unnatural spurious minutiae pairings while simultaneously using path or ridge orientations to increase performance and similarity score calculation. Thus the obtained processed images can be used in biometric purposes which in turn enhances the security of the syste

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

Biometrics beyond the visible spectrum: Imaging technologies and applications

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04391-8_20Proceedings of Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid (Spain)Human body images acquired at visible spectrum have inherent restrictions that hinder the performance of person recognition systems built using that kind of information (e.g. scene artefacts under varying illumination conditions). One promising approach for dealing with those limitations is using images acquired beyond the visible spectrum. This paper reviews some of the existing human body imaging technologies working beyond the visible spectrum (X-ray, Infrared, Millimeter and Submillimeter Wave imaging technologies). The benefits and drawbacks of each technology and their biometric applications are presented.This work has been supported by Terasense (CSD2008-00068) Consolider project of the Spanish Ministry of Science and Technology

Versatile and Economical Acquisition Setup for Dorsa Palm Vein Authentication

AbstractVarious biometrics were employed in many applications for security purposes, amongst palm vein biometrics is one of the best methods for unique identification of a person owing to the indestructible quality of the inner vein structures. In this paper, we have proposed our own setup for capturing vein structures of human dorsal palm using a web camera modified into a near infrared camera. The illumination for capturing images is provided with the help of 30 Infrared LEDs. The objective of this paper is to produce a versatile and an economical way for obtaining vein images rather than using a high priced Near Infrared Camera and can easily deployed in any small scale applications. This setup can be used to acquire finger veins too simultaneously. We have modified the web camera by removing the infrared filter present in it and replacing it with a visible light filter. The quality and performance of the newly acquired samples are tested with two different feature extraction methods namely Correlation filter and Speeded Up Robust Features (SURF) algorithm. Correlation method has obtained very good results than SURF in identifying the genuine samples