Robust local oriented patterns for ear recognition

Abstract

Extraction and description of image features is an active research topic and important for several applications of computer vision field. This paper presents a new noise-tolerant and rotation-invariant local feature descriptor called robust local oriented patterns (RLOP). The proposed descriptor extracts local image structures utilizing edge directional information to provide rotation-invariant patterns and to be effective in noise and changing illumination situations. This is achieved by a non-linear amalgamation of two specific strategies; binarizing neighborhood pixels of a patch and assigning binomial weights in the same formula. In the encoding methodology, the neighboring pixels is binarized with respect to the mean value of pixels in an image patch of size 3 × 3 instead of the center pixel. Thus, the obtained codes are rotation-invariant and more robust against noise and other non-monotonic grayscale variations. Ear recognition is considered as the representative problem, where the ear involves localized patterns and textures. The proposed descriptor encodes all images’ pixels and the resulting RLOP-encoded image is divided into several regions. Histograms of the regions are constructed to estimate the distribution of features. Then, all histograms are concatenated together to form the final descriptor. The robustness and effectiveness of the proposed descriptor are evaluated through conducting several identification and verification experiments on four different ear databases: IIT Delhi-I, IIT Delhi-II, AMI, and AWE. It is observed that the proposed descriptor outperforms the state-of-the-art texture based approaches achieving a recognition rate of 98% on the average providing the best performance among the tested descriptors.