Region Based Extensive Response Index Pattern for Facial Expression Recognition

This paper presents a novel descriptor named Region based Extensive Response Index Pattern (RETRaIN) for facial expression recognition. The RETRaIN encodes the relation among the reference and neighboring pixels of facial active regions. These relations are computed by using directional compass mask on an input image and extract the high edge responses in foremost directions. Further extreme edge index positions are selected and encoded into six-bit compact code to reduce feature dimensionality and distinguish between the uniform and non-uniform patterns in the facial features. The performance of the proposed descriptor is tested and evaluated on three benchmark datasets Extended Cohn Kanade, JAFFE, and MUG. The RETRaIN achieves superior recognition accuracy in comparison to state-of-the-art techniques.

QUEST: Quadriletral Senary bit Pattern for Facial Expression Recognition

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.