Local Directional Relation Pattern for Unconstrained and Robust Face Retrieval

TL;DR

The paper introduces a novel local directional relation pattern (LDRP) descriptor that encodes wider local relationships for robust face retrieval, outperforming existing descriptors and deep learning models in unconstrained environments.

Contribution

A new local descriptor, LDRP, efficiently encodes directional neighbor relationships, enhancing discriminative power and robustness in face recognition tasks.

Findings

LDRP outperforms state-of-the-art descriptors on multiple face databases.

LDRP surpasses pre-trained CNN models and DLib face descriptors in various scenarios.

The multi-scale LDRP improves face retrieval accuracy under challenging conditions.

Abstract

Face recognition is still a very demanding area of research. This problem becomes more challenging in unconstrained environment and in the presence of several variations like pose, illumination, expression, etc. Local descriptors are widely used for this task. The most of the existing local descriptors consider only few immediate local neighbors and not able to utilize the wider local information to make the descriptor more discriminative. The wider local information based descriptors mainly suffer due to the increased dimensionality. In this paper, this problem is solved by encoding the relationship among directional neighbors in an efficient manner. The relationship between the center pixel and the encoded directional neighbors is utilized further to form the proposed local directional relation pattern (LDRP). The descriptor is inherently uniform illumination invariant. The multi-scale mechanism is also adapted to further boost the discriminative ability of the descriptor. The proposed descriptor is evaluated under the image retrieval framework over face databases. Very challenging databases like PaSC, LFW, PubFig, ESSEX, FERET, AT&T, and FaceScrub are used to test the discriminative ability and robustness of LDRP descriptor. Results are also compared with the recent state-of-the-art face descriptors such as LBP, LTP, LDP, LDN, LVP, DCP, LDGP and LGHP. Very promising performance is observed using the proposed descriptor over very appealing face databases as compared to the existing face descriptors. The proposed LDRP descriptor also outperforms the pre-trained ImageNet CNN models over large-scale FaceScrub face dataset. Moreover, it also outperforms the deep learning based DLib face descriptor in many scenarios.