基于改进型嵌入式隐马尔可夫模型的表情识别方法

Facial Expression Recognition Using an Improved Embedded HMM

提出一种基于改进型嵌入式隐马尔可夫模型的表情识别方法.首先通过视频人脸跟踪检验获取关键帧的感兴趣区域.然后利用二维离散余弦变换将人脸图像观测块转化为观测向量.最后实现嵌入式隐马尔可夫进行模型训练与表情识别.实验表明,采用嵌入式隐马尔可夫模型可有效识别表情,改进和优化后的设计方案识别效果良好.

An embedded hidden markov model （e-HMM） based approach for facial expression recognition is proposed. It makes use of an optimized set of observation vectors obtained from the 2D-DCT coefficients of the facial region of interest. The e-HMM is trained with segmental K-means algorithm and used for the facial expression recognition. The experimental results show the remarkable improvement of the performance and robustness of the facial expression recognition system.