Periocular biometrics: databases, algorithms and directions

TL;DR

This paper reviews the current state of periocular biometrics, discussing databases, algorithms, and future research directions, highlighting its advantages and applications in biometric recognition and classification tasks.

Contribution

It provides a comprehensive survey of periocular biometric research, covering existing databases, algorithms, and potential future developments in the field.

Findings

Periocular biometrics is effective in uncontrolled conditions.

The periocular region can be used for gender and ethnicity classification.

Future research should focus on improving algorithms and expanding datasets.

Abstract

Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. Periocular refers to the facial region in the eye vicinity, including eyelids, lashes and eyebrows. It is available over a wide range of acquisition distances, representing a trade-off between the whole face (which can be occluded at close distances) and the iris texture (which do not have enough resolution at long distances). Since the periocular region appears in face or iris images, it can be used also in conjunction with these modalities. Features extracted from the periocular region have been also used successfully for gender classification and ethnicity classification, and to study the impact of gender transformation or plastic surgery in the recognition performance. This paper presents a review of the state of the art in periocular biometric research, providing an insight of the most relevant issues and giving a thorough coverage of the existing literature. Future research trends are also briefly discussed.