Human identification from at-a-distance images by simultaneously exploiting iris and periocular features

Chun Wei Tan, Ajay Kumar Pathak

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

31 Citations (Scopus)


Iris recognition from at-a-distance face images has high applications in wide range of applications such as remote surveillance and for civilian identification. This paper presents a completely automated joint iris and periocular recognition approach from the face images acquired at-a-distance. Each of the acquired face images are used to detect and segment periocular images which are then employed for the iris segmentation. We employ complex texture descriptors using Leung-Mallik filters which can acquire multiple periocular features for more accurate recognition. Experimental results presented in this paper achieve 8.1% improvement in recognition accuracy over the best performing approach among SIFT, LBP and HoG presented in the literature. The combination of simultaneously segmented iris and periocular images achieves average rank-one recognition accuracy of 84.5%, i.e., an improvement of 52% than those from only using iris features, on independent test images from 131 subjects. In order to ensure the repeatability of the experiments, the CASIA.v4-distance, i.e., a publicly available database was employed and all the 142 subjects/images were considered in this work.
Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Number of pages4
Publication statusPublished - 1 Dec 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012


Conference21st International Conference on Pattern Recognition, ICPR 2012

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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