Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval

TL;DR

The paper introduces a novel local gradient hexa pattern (LGHP) descriptor that captures complex local relationships in facial images, improving face recognition and retrieval accuracy across various challenging datasets.

Contribution

A new LGHP descriptor is proposed that effectively encodes local neighborhood relationships for enhanced face recognition and retrieval performance.

Findings

LGHP outperforms LDP and LVP on benchmark datasets

Improved recognition and retrieval rates across multiple databases

Effective in varying pose, illumination, and lighting conditions

Abstract

Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination and lighting conditions. Accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures. In this paper a local gradient hexa pattern (LGHP) is proposed that identifies the relationship amongst the reference pixel and its neighboring pixels at different distances across different derivative directions. Discriminative information exists in the local neighborhood as well as in different derivative directions. Proposed descriptor effectively transforms these relationships into binary micropatterns discriminating interclass facial images with optimal precision. Recognition and retrieval performance of the proposed descriptor has been compared with state-of-the-art descriptors namely LDP and LVP over the most challenging and benchmark facial image databases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, and LFW. The proposed descriptor has better recognition as well as retrieval rates compared to state-of-the-art descriptors.