Weighted Feature Gaussian Kernel SVM for Emotion Recognition.
Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Wei
0000-0002-8439-2296
W
Wei
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Qingxuan
Q
Jia
D000465:Algorithms; D004644:Emotions / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D021641:Recognition, Psychology; D060388:Support Vector Machine