Compensating for pose and illumination in unconstrained periocular biometrics
In the context of less constrained biometrics recognition, the use of information from the vicinity of the eyes (periocular) is considered with high potential and motivated several recent proposals. In this paper, we focus on two factors that are known to degrade the performance of periocular recognition: varying illumination conditions and subjects pose. Hence, this paper has three major purposes: 1) describe the decreases in performance due to varying illumination and subjects poses; 2) propose two techniques to improve the robustness to these factors; 3) announce the availability of an annotated dataset of periocular data (UBIPosePr), where poses vary in regular intervals, turning it especially suitable to assess the effects of misalignments between camera and subjects in periocular recognition. He has authored around ten research papers in international conferences. His research interests include signal and image processing, computer vision and pattern recognition, computational intelligence and biometric systems. He is author of over 50 publications. He is the Associate Editor (for ocular biometrics area) of the IEEE Biometrics Compendium Journal and member of the editorial board of the International Journal of Biometrics. Also, he served as the Guest-Editor of special issues of the pattern recognition letters, image and vision computing and signal, image and video processing journals.