一种多尺度可变形部件模型的人脸表情识别

Facial Expression Recognition Based on Multi-scale Deformable Part Model

针对现有表情识别研究无法精确捕捉脸部关键部位特征,提出一种多尺度可变形部件模型(DPM)的人脸表情识别方法。首先构建多尺度图像的特征金字塔,然后用随机梯度下降算法训练人脸DPM模型,根据DPM模型中根滤波器与部件滤波器的响应值确定人脸关键部位位置,最后提取关键部位的HOG特征,将获得的特征输入到分类器中训练。在CK+和JAFFE表情库上的验证结果表明,该方法在不同角度和光照强弱影响下对人脸均有较好的检测和定位效果,提取的人脸关键部位特征在计算速率和识别率上优于对比算法。

In view of the problem that the existed research on facial expression recognition could not capture the features of key facial parts,a facial expression recognition approach based on multi-scale deformable part model( DPM) was proposed. Firstly,a feature pyramid of multi-scale image was built. And then,by means of random gradient descent methods the multi-scaled facial deformable part model was obtained. According to the response value of root filter and parts filter on the feature pyramid the face and its key parts were detected. In final,the HOG features on the key facial parts were extracted. Input it into classifier to train a model. The experimental results of CK + and JAFFE expression databases show that under various face angles and illumination conditions,the proposed approach maintains the better effect in aspects of face detection and its key parts positioning,and furthermore,the computing speed and recognition rate of facial expression is superior to other comparison algorithms.