  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.
