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

TL;DR

This paper introduces the Centre Symmetric Quadruple Pattern (CSQP), a novel hand-crafted facial descriptor that effectively encodes larger neighborhoods to improve recognition and retrieval accuracy under varied conditions.

Contribution

The paper proposes CSQP, a structurally symmetric descriptor that encodes facial asymmetry efficiently, capturing more meaningful information with fewer binary bits compared to existing descriptors.

Findings

CSQP outperforms state-of-the-art descriptors on benchmark datasets.

It maintains high accuracy under uncontrolled environmental variations.

The descriptor encodes larger neighborhoods efficiently, reducing feature length.

Abstract

Facial features are defined as the local relationships that exist amongst the pixels of a facial image. Hand-crafted descriptors identify the relationships of the pixels in the local neighbourhood defined by the kernel. Kernel is a two dimensional matrix which is moved across the facial image. Distinctive information captured by the kernel with limited number of pixel achieves satisfactory recognition and retrieval accuracies on facial images taken under constrained environment (controlled variations in light, pose, expressions, and background). To achieve similar accuracies under unconstrained environment local neighbourhood has to be increased, in order to encode more pixels. Increasing local neighbourhood also increases the feature length of the descriptor. In this paper we propose a hand-crafted descriptor namely Centre Symmetric Quadruple Pattern (CSQP), which is structurally symmetric and encodes the facial asymmetry in quadruple space. The proposed descriptor efficiently encodes larger neighbourhood with optimal number of binary bits. It has been shown using average entropy, computed over feature images encoded with the proposed descriptor, that the CSQP captures more meaningful information as compared to state of the art descriptors. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand-crafted descriptors (CSLBP, CSLTP, LDP, LBP, SLBP and LDGP) on bench mark databases namely; LFW, Colour-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under controlled as well as uncontrolled variations in pose, illumination, background and expressions.