Facial expression recognition based on improved completed local ternary patterns

Abstract:

The information of expression texture extracted by the completed local ternary patterns (CLTP) method is not accurate enough, which may cause low recognition rate. Therefore, an improved completed local ternary patterns (ICLTP) is proposed here. Firstly, the Scharr operator is used to calculate gradient magnitudes of images to enhance the detail of texture, which is beneficial to obtaining more accurate expression features. Secondly, two different neighborhoods of CLTP features are combined to obtain much information of facial expression. Finally, K nearest neighbor (KNN) and sparse representation classifier (SRC) are combined for classification and a 10-fold cross-validation method is tested in the JAFFE and CK+ databases. The results show that the ICLTP method can improve the recognition rate of facial expression and reduce the confusion between various expressions. Especially, the misrecognition rate of other six expressions recognized as neutral is reduced in the 7-class expression recognition.