Video-based facial expression recognition by removing the style variations

This study examines the performance of person-independent facial expression recognition improved by adapting the system to a given person. The proposed method transfers the style of particular subjects to the semi-style-free space. There is no need to change the person-independent classifier in order to improve the performance. The style transfer mapping (STM) has been proposed in image-based classification. The challenges of employing this technique in video-based facial expression recognition are: estimating STM from image sequences of each subject (adaptation data) and projecting new sequential data of each subject in semi-style-free space. A mixture of `binary support vector machines´ and `hidden Markov models´ were employed to overcome these challenges. Moreover, virtual samples generated by using the person´s neutral samples were used to estimate STM. Experimental results on the CK+ database confirm the efficiency of the proposed method in recognition rate improvement.