基于特征融合与决策树技术的表情识别方法

A facial expression recognition algorithm based on feature fusion and hierarchical decision tree technology

针对复杂状况下传统表情识别方法存在的问题,提出一种新的非特定人表情识别方法。该算法首先提取每张表情图像的HOG特征和Haar小波特征,然后将两种不同的特征串行融合得到整幅图像的特征,最后通过SVM多分类器完成各层人脸表情的分类识别。在JAFFE人脸表情库上的仿真实验中,该方法的分类准确率达到87.9%,平均时耗达到10.296 6s。对比实验结果表明,所提算法具有更高的识别率、更好的实时性和更强的鲁棒性。

Aiming at the deficiency of the traditional expression recognition methods for complex situation, we propose a novel person-independent facial expression recognition method. We first calculate the histogram of orientation gradient for each facial expression image and extract Haar wavelet features from them. Then by connecting the two different characteristics obtained above, we get the whole image features. By using the multi-class SVM classifier in each layer we finally achieve facial expression classification and recognition. Simulations of facial expression recognition on the Japanese Female Facial Expression （JAFFE） database show that the classification accuracy reaches up to 87.9%, and the average time consumption rises to 10. 2966s. The results of comparison experiments show that the new algorithm has higher recognition accuracy rate, less time consumption and stronger robustness.