QUEST: Quadriletral Senary bit Pattern for Facial Expression Recognition

TL;DR

This paper introduces the QUEST pattern, a novel six-bit feature descriptor that improves facial expression recognition accuracy by capturing intensity changes, handling viewpoint and illumination variations, and enhancing robustness against noise.

Contribution

The paper proposes the Quadrilateral Senary bit Pattern (QUEST), a new feature descriptor that enhances facial expression recognition by emphasizing local intensity transitions and structural relationships.

Findings

Outperforms existing methods on benchmark datasets

Improves robustness to noise, viewpoint, and illumination variations

Achieves higher classification accuracy in experiments

Abstract

Facial expression has a significant role in analyzing human cognitive state. Deriving an accurate facial appearance representation is a critical task for an automatic facial expression recognition application. This paper provides a new feature descriptor named as Quadrilateral Senary bit Pattern for facial expression recognition. The QUEST pattern encoded the intensity changes by emphasizing the relationship between neighboring and reference pixels by dividing them into two quadrilaterals in a local neighborhood. Thus, the resultant gradient edges reveal the transitional variation information, that improves the classification rate by discriminating expression classes. Moreover, it also enhances the capability of the descriptor to deal with viewpoint variations and illumination changes. The trine relationship in a quadrilateral structure helps to extract the expressive edges and suppressing noise elements to enhance the robustness to noisy conditions. The QUEST pattern generates a six-bit compact code, which improves the efficiency of the FER system with more discriminability. The effectiveness of the proposed method is evaluated by conducting several experiments on four benchmark datasets: MMI, GEMEP-FERA, OULU-CASIA, and ISED. The experimental results show better performance of the proposed method as compared to existing state-art-the approaches.