BiSpectral contactless hand based biometric system

Abstract

This paper presents a contactless hand based biometric identification system using geometric and palm features. Hand images are acquired using two commercial webcams with 1200×1600 pixel resolution which are refered to as the “IR” and “visible” webcams. The IR webcam has been modified by exchanging the IR filter with a visible filter lens and reducing the gain and exposure time to improve the hand contour extraction. The hand was illuminated using 24 infra-red LEDs and 4 white light LEDs. Images acquired from the IR webcam were binarized and the normalized widths from the index to little finger were used as features. A Least Square Support Vector Machine was then used for verification. The palm features were obtained by the Orthogonal Line Ordinal Features approach applied to the image acquired by the visible webcam. The hand image from the visible webcam was segmented using an Active Shape Model guided by the hand contour from the IR webcam as an initial guest. A Hamming distance was used as verifier. More than 8000 hand images from three public databases were used in order to compare the features extraction approaches. A score level fusion of both biometrics is performed obtaining an Equal Error Rate of 0.17% with a proprietary database of 100 users acquired with the proposed device.