Region specific and subimage based neighbour gradient feature extraction for robust periocular recognition

Abstract

This paper proposes a Periocular recognition algorithm that uses region-specific and sub-image-based neighbor gradient feature extraction to achieve better recognition results. This approach initially segments the periocular region into four sub-regions such as eyebrow, eye corner regions, upper and lower eye fold regions after detecting the left and right eye corner points. KAZE feature extraction algorithm is used to extract the features from the upper eye fold region, while the HOG feature extraction algorithm is used to extract the feature from the eyebrow and eye corner regions. This approach also estimates the shape of the eyebrow by estimating the distance from N points on the eyebrow region to the eye corner midpoint. The eyebrow shape feature also contains the width and height measures through N points on the eyebrow that give its shape. The proposed approach also proposes a sub-image-based neighbor gradient (SING) feature extraction that extracts the neighbor gradient features from a 3×3 sub-image. Finally, the extracted features are trained using the Naïve Bayes classifier. The experimental evaluation was done using the AR dataset, CASIA Iris distance dataset, and UBIPr dataset using the metrics such as rank-1, rank-5 recognition accuracies, the area under the ROC curve (AUC), and equal error rate (EER). The proposed scheme provides a rank-1 recognition rate of 92.32 %, 97.41 %, and 97.87 % for the dataset UBIPr, CASIA-Iris, and AR datasets respectively. The experimental results reveal that the proposed method outperforms the traditional periocular recognition algorithms.