Facial expression recognition based on Gabor features of salient patches and ACI-LBP

Texture features of the salient patches are closely related to the facial expression recognition on face images. To obtain these features, we applied the Gabor wavelets to extract the relevant values on the whole-face and important regions such as the eyes, nose, and mouth of the face, and assigned different weights to them with respect to their different recognition effectiveness. Since the LBP operator is largely dependent on the center pixel and is easily to be affected by the lighting conditions, an Around Center Instable Local Binary Pattern (ACI-LBP) operator is applied in this research. The technique takes consideration of the relationship between the center point and the adjacent points, thus extends the representations of the fetures in the local region and is more robust to noise and illumination. To get the ACI-LBP, the LBP value is calculated first, then the Near Local Binary Pattern (N-LBP) value is calculated based on the distance between each pixel point and its neighborhood points in clockwise direction. The inconsistent values of LBP and N-LBP in corresponding positions are calculated in terms of their absolute values. In addition, a multi-scale histogram statistics method is adopted in the ACI-LBP extraction. Finally, the two parts features, Gabor and ACI-LBP, are merged as an integrated feature vector to classify and recognize the facial expression. Experimental results based on the JAFFE and CK facial databases show that the method can effectively improve the recognition accuracy of the facial expression recognition.