关键词: 情感识别, 移动应用, Bandlet变换, 局部二进制值模式, Kruskal-Wallis检验

Abstract: Because of the intelligence and user receptivity of mobile applications, emotion aware mobile applications are increasing. Because of the limited processing power of mobile devices, algorithms for emotion recognition on mobile devices should be implemented in real time and efficiently. This paper proposes a method for emotion recognition with high accuracy and low computational complexity in mobile applications. In this method, first, the face video is captured by a smartphone camera. Some representative frames are extracted from the video, and a human face detection module is used to extract face regions from these frames. Then, the face region is processed by Bandlet transform, and the resultant subband is divided into nonoverlapping blocks. Local binary patterns’ histograms are calculated for each block, and the histogram of all blocks is connected as a feature set to describe the facial image. Finally, Kruskal-Wallis test is used to select the most dominant features from the facial image feature sets, and these features are fed into the Gauss mixture model classifier for emotion recognition.Experimental results show that the proposed method achieves high recognition accuracy in a reasonable time.

Key words: emotion recognition, mobile applications, Bandlet transform, local binary patterns, Kruskal-Wallis test