Texture based feature extraction using symbol patterns for facial expression recognition

Facial expressions can convey the internal emotions of a person within a certain scenario and play a major role in the social interaction of human beings. In automatic Facial Expression Recognition (FER) systems, the method applied for feature extraction plays a major role in determining the performance of a system. In this regard, by drawing inspiration from the Swastik symbol, three texture based feature descriptors named Symbol Patterns (SP₁, SP₂ and SP₃) have been proposed for facial feature extraction. SP₁ generates one pattern value by comparing eight pixels within a 3$$3 neighborhood, whereas, SP₂ and SP₃ generates two pattern values each by comparing twelve and sixteen pixels within a 5$$5 neighborhood respectively. In this work, the proposed Symbol Patterns (SP) have been evaluated with natural, fibonacci, odd, prime, squares and binary weights for determining the optimal recognition accuracy. The proposed SP methods have been tested on MUG, TFEID, CK+, KDEF, FER2013 and FERG datasets and the results from the experimental analysis demonstrated an improvement in the recognition accuracy when compared to the existing FER methods.