Periocular Images Research Articles

Appearance-based periocular features in the context of face and non-ideal iris recognition

Developing newer approaches to deal with non-ideal scenarios in face and iris biometrics has been a key focus of research in recent years. The same reason motivates the study of the periocular biometrics as its use has a potential of significantly impacting the iris- and face-based recognition. In this paper, we explore the utility of the various appearance features extracted from the periocular region from different perspectives: (i) as an independent biometric modality for human identification, (ii) as a tool that can aid iris recognition in non-ideal situations in the near infra-red (NIR) spectrum, and (iii) as a possible partial face recognition technique in the visible spectrum. We employ a local appearance-based feature representation, where the periocular image is divided into spatially salient patches, appearance features are computed for each patch locally, and the local features are combined to describe the entire image. The images are matched by computing the distance between the corresponding feature representations using various distance metrics. The evaluation of the periocular region-based recognition and comparison to face recognition is performed in the visible spectrum using the FRGC face dataset. For fusion of the periocular and iris modality, we use the MBGC NIR face videos. We demonstrate that in certain non-ideal conditions encountered in our experiments, the periocular biometrics is superior to iris in the NIR spectrum. Furthermore, we also demonstrate that recognition performance of the periocular region images is comparable to that of face in the visible spectrum.

Periocular Biometrics in the Visible Spectrum

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.