Facial expression recognition using distance and shape signature features

Abstract

• Distance and Shape signatures, individually and combined, appear as new contribution. • Stability indices corresponding to Distance and Shape have been proposed. • Signatures and Stability indices and statistical metrics of relevance form the feature set. • MLP is learnt with this feature set for emotion estimation from face images. • Results obtained thereof are extremely encouraging. Distance and Shape signature features in human faces offer formidable significance in recognizing facial expressions. Identifying appropriate landmarks is a crucial as well as challenging issue in the field of expression recognition of human faces. Appearance model has been found useful to detect the salient landmarks on human faces. These salient landmarks induce a grid on the human face along with the formation of possible triangles joining the grid. Normalized distance and shape signatures are determined from the grid. Distance signature as well as shape signature find respective stability indices which play important role to recognize the facial expressions. Statistical measures such as range, moment, skewness, kurtosis and entropy are calculated from normalized distance and shape signature pair to supplement the feature set. This enhanced feature set is fed into a Multilayer Perceptron (MLP) to arrive at different expression categories encompassing anger, sadness, fear, disgust, surprise and happy. We investigated our proposed system on Cohn-Kanade (CK+), JAFFE, MMI and MUG databases to conduct and validate our experiment and establish its performance superiority over other existing competitors.