Influence of shape and texture features on facial expression recognition
Asit Barman, Paramartha Dutta · IET Image Processing · 2019
Shape and texture features provide a novel framework for expression recognition by using salient landmarks based triangle initialisation and identification of texture regions. Detection of effective landmarks is achieved by well‐known appearance‐based model to form a grid. Accordingly, several triangles are identified within the grid with respect to the nose landmark reference point. These salient landmarks are also used to find the texture regions. Normalised shape and texture signatures are derived from triangles and texture regions. Stability indices are determined from shape and texture signatures which are also exploited as significant features for recognition of facial expressions. Statistical measures such as range, moment, skewness, kurtosis, and entropy are used to supplement the feature set. Individual and combined features are fed into multilayer perceptron and deep belief network (DBN) network to classify different expression categories encompassing anger, sadness, fear, disgust, surprise, and happiness. The authors investigated the performance of their proposed system on Cohn–Kanade, JAFFE, MMI, MUG, and Wild face benchmark databases. Thorough experimentation establishes the performance superiority of the proposed methodology over other existing competitors. Combining features is also validated through extensive results and analyses.