Local Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval

TL;DR

This paper introduces a new local quadruple pattern (LQPAT) descriptor for facial recognition that encodes local relationships efficiently, improving robustness under uncontrolled environmental variations.

Contribution

The paper presents a novel hand-crafted facial descriptor that encodes relations among neighbors in quadruple space with optimal feature length, outperforming existing descriptors.

Findings

Achieves higher recognition accuracy on benchmark datasets.

Performs robustly under pose, illumination, and expression variations.

Outperforms state-of-the-art descriptors in experiments.

Abstract

In this paper a novel hand crafted local quadruple pattern (LQPAT) is proposed for facial image recognition and retrieval. Most of the existing hand-crafted descriptors encodes only a limited number of pixels in the local neighbourhood. Under unconstrained environment the performance of these descriptors tends to degrade drastically. The major problem in increasing the local neighbourhood is that, it also increases the feature length of the descriptor. The proposed descriptor try to overcome these problems by defining an efficient encoding structure with optimal feature length. The proposed descriptor encodes relations amongst the neighbours in quadruple space. Two micro patterns are computed from the local relationships to form the descriptor. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand crafted descriptors on bench mark databases namely; Caltech-face, LFW, Colour-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under uncontrolled variations in pose, illumination, background and expressions.