基于视频的人脸表情识别方法研究

近年来，表情识别逐渐成为计算机视觉和模式识别领域的研究热点之一。给出了一个包含人脸特征提取和表情识别的计算机视觉系统，通过对视频中人脸兼容运动特征的跟踪，提取人脸运动特征向量序列，与以往的方法不同，提取到的特征向量流被分割为两类，一类是表情特征向量流，另一类是视觉语音特征向量流。然后，利用基于CHMM（Couple Hidden Markov Model）的表情识别模型，进行人脸表情的识别，该模型允许两个向量流根据其各自的时域特征以异步方式进行处理，同时保持这两个向量流在时域上的自然关联。实验表明该方法优于传统的单通道处理方法。

Approach on video-based expression recognition

Expression recognition is becoming one of the latest challenges in computer vision and pattern recognition in recent years.In this paper,a computer vision system including both facial feature extraction and expression recognition is proposed.Facial motion features tracking and extraction are performed on the emotional speaking video first.Different with previous approach, segment the extracted feature vectors stream into two classifications.One is expression feature vector stream.The other is the visual speech one.Then,based on CHMM(Coupled Hidden Markov Model),which keeps the state asynchrony of the two observation sequences while preserving their natural correlation through the process,expression recognition is performed.The experimental results show that the coupled HMM outperforms the multi-stream HMM in video based emotion recognition.