A comprehensive approach for skin recognition
International Journal of Biometrics
Multispectral scleral patterns for ocular biometric recognition
Pattern Recognition Letters
Periocular recognition using retinotopic sampling and gabor decomposition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Robust periocular recognition by fusing local to holistic sparse representations
Proceedings of the 6th International Conference on Security of Information and Networks
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The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.