R-Theta Local Neighborhood Pattern for Unconstrained Facial Image Recognition and Retrieval

TL;DR

This paper introduces RTLNP, a novel facial image descriptor that encodes local pixel relationships in angular and radial sectors, outperforming existing methods across multiple facial image datasets including unconstrained and NIR images.

Contribution

RTLNP is a new local neighborhood pattern encoding scheme that captures pixel relationships in sectors, demonstrating superior retrieval performance over existing descriptors.

Findings

RTLNP outperforms state-of-the-art descriptors on multiple facial datasets.

RTLNP shows high effectiveness in unconstrained and NIR facial image retrieval.

The descriptor is robust across various challenging facial image databases.

Abstract

In this paper R-Theta Local Neighborhood Pattern (RTLNP) is proposed for facial image retrieval. RTLNP exploits relationships amongst the pixels in local neighborhood of the reference pixel at different angular and radial widths. The proposed encoding scheme divides the local neighborhood into sectors of equal angular width. These sectors are again divided into subsectors of two radial widths. Average grayscales values of these two subsectors are encoded to generate the micropatterns. Performance of the proposed descriptor has been evaluated and results are compared with the state of the art descriptors e.g. LBP, LTP, CSLBP, CSLTP, Sobel-LBP, LTCoP, LMeP, LDP, LTrP, MBLBP, BRINT and SLBP. The most challenging facial constrained and unconstrained databases, namely; AT&T, CARIA-Face-V5-Cropped, LFW, and Color FERET have been used for showing the efficiency of the proposed descriptor. Proposed descriptor is also tested on near infrared (NIR) face databases; CASIA NIR-VIS 2.0 and PolyU-NIRFD to explore its potential with respect to NIR facial images. Better retrieval rates of RTLNP as compared to the existing state of the art descriptors show the effectiveness of the descriptor