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.