Local Directional Gradient Pattern: A Local Descriptor for Face Recognition

TL;DR

This paper introduces a novel local descriptor called LDGP for face recognition, which encodes relationships between high order derivatives in multiple directions, reducing computational time while maintaining high recognition accuracy.

Contribution

The paper proposes LDGP, a high-order derivative-based local descriptor that is more efficient in face recognition tasks compared to existing methods.

Findings

Reduces feature extraction and matching time significantly.

Maintains recognition accuracy comparable to state-of-the-art methods.

Validated on benchmark face databases with positive results.

Abstract

In this paper a local pattern descriptor in high order derivative space is proposed for face recognition. The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between the higher order derivatives of the reference pixel in four distinct directions. The proposed descriptor identifies the relationship between the high order derivatives of the referenced pixel in four different directions to compute the micropattern which corresponds to the local feature. Proposed descriptor considerably reduces the length of the micropattern which consequently reduces the extraction time and matching time while maintaining the recognition rate. Results of the extensive experiments conducted on benchmark databases AT&T, Extended Yale B and CMU-PIE show that the proposed descriptor significantly reduces the extraction as well as matching time while the recognition rate is almost similar to the existing state of the art methods.