Periocular Region-Based Biometric Identification Using Local Binary Pattern and Its Variants

Abstract

Biometric systems are gaining importance significantly in the present day automated systems especially in the areas of authentication, access control, security, and forensic applications for the identification of criminals. Existing biometric systems using the face and iris regions had reached the state of maturity with almost having high performances of 100% accuracy provided the images of the subjects are acquired in the cooperative scenarios. The periocular region is one of the most promising biometric traits providing better robustness and high discrimination ability. Periocular region-based biometric recognition systems are well suited for the wild environments where the subjects are not cooperative. In the proposed paper, the pixel-based LBP and patch-based LBP variants are used as local descriptors for the feature extraction of discriminative features from the full face and periocular regions. Euclidean distance is used to find the matching score between two extracted feature vectors. The experimentation is performed on FRGC, FERET, and Georgia Tech face databases to compare the performance of both periocular and face biometric modalities. It showed that the periocular region has almost the same level of performance of the face region using only 25% data of the complete face.