BRISK 기반의 눈 영상을 이용한 사람 인식

Abstract

Ocular region recently emerged as a new biometric trait for overcoming the limitations of iris recognition performance at the situation that cannot expect high user cooperation, because the acquisition of an ocular image does not require high user cooperation and close capture unlike an iris image. This study proposes a new method for ocular image recognition based on BRISK (binary robust invariant scalable keypoints). It uses the distance ratio of the two nearest neighbors to improve the accuracy of the detection of corresponding keypoint pairs, and it also uses geometric constraint for eliminating incorrect keypoint pairs. Experiments for evaluating the validity the proposed method were performed on MMU public database. The person recognition rate on left and right ocular image datasets showed 91.1% and 90.6% respectively. The performance represents about 5% higher accuracy than the SIFT-based method which has been widely used in a biometric field.