摘要
The challenge of coping with non-frontal head poses during facial expression recognition results in considerable reduction of accuracy and robustness when capturing expressions that occur during natural communications. In this paper, we attempt to recognize facial expressions under poses with large rotation angles from 2D videos. A depth^patch based 4D expression representation model is proposed. It was reconstructed from 2D dynamic images for delineating continuous spatial changes and temporal context under non-frontal cases. Furthermore, we present an effective deep neural network classifier, which can accurately capture pose-variant expression features from the depth patches and recognize non-frontal expressions. Experimental results on the BU-4DFE database show that the proposed method achieves a high recognition accuracy of 86.87% for non-frontal facial expressions within a range of head rotation angle of up to 52%, outperforming existing methods. We also present a quantitative analysis of the components contributing to the performance gain through tests on the BU-4DFE and Multi-PIE datasets.
The challenge of coping with non-frontal head poses during facial expression recognition results in considerable reduction of accuracy and robustness when capturing expressions that occur during natural communications. In this paper, we attempt to recognize facial expressions under poses with large rotation angles from 2D videos. A depth^patch based 4D expression representation model is proposed. It was reconstructed from 2D dynamic images for delineating continuous spatial changes and temporal context under non-frontal cases. Furthermore, we present an effective deep neural network classifier, which can accurately capture pose-variant expression features from the depth patches and recognize non-frontal expressions. Experimental results on the BU-4DFE database show that the proposed method achieves a high recognition accuracy of 86.87% for non-frontal facial expressions within a range of head rotation angle of up to 52%, outperforming existing methods. We also present a quantitative analysis of the components contributing to the performance gain through tests on the BU-4DFE and Multi-PIE datasets.
基金
This work was supported by the National Key Research and Development Program of China under Grant No. 2016YFBI001405, and the National Natural Science Foundation of China under Grant Nos. 61232013, 61422212, and 61661146002.
