精确局部特征描述的表情识别

Precise local feature description for facial expression recognition

目的针对传统局部特征提取方法在表情识别上的局限性,提出一种精确局部特征描述的表情识别方法。方法首先将人的眉毛、眼睛和嘴巴3个对表情识别起关键作用的器官分割出来,使得特征描述更具有针对性。然后,构造充分矢量三角形以统计图像的轮廓特征与细节特征。最后,对于不同的表情器官采用不同尺度的充分矢量三角形描述,对于同种表情器官采用多种尺度的充分矢量三角形联合描述,从而充分描述关键器官的图像信息。结果该算法在日本女性表情人脸库(JAFFE)、cohn-kanade库(CK)以及Pain expressions库上进行实验,分别取得了95.67%、97.83%、84.0%的平均识别率以及11.70 ms、30.23 ms、11.73 ms的平均特征提取时间,实验结果表明,精确局部特征描述的表情识别方法可以较快、较准确的进行人脸表情识别。结论精确局部特征描述的表情识别方法通过器官的分割以及充分矢量三角形模式的构造与灵活运用,良好地表达了图像的局部特征且具有较低的时间复杂度,本文算法与目前典型的表情识别算法的实验对比结果也表明了本文算法的有效性。

Objective To identify facial expressions accurately, we propose a precise local feature description method for fa- cial expression recognition. Method First, the eyebrows, eyes, and mouth in a facial expression image are identified and extracted. The local features from the organ images are then obtained and processed by the expanded vector triangle pattern. The outline and detail features of the images can be statistic. Finally, different scales of sufficient vector triangle patterns are used to describe the features of the different organs. Various scales of sufficient vector triangle patterns are then combined to describe the features of the same organ. In this way, key organ information can be expressed fully. Result Experiments on the proposed method were performed using the JAFEE, Cohn-Kanade （CK） , and Pain Expressions database. The average recog- nition rates were 95.67% , 97. 83% , and 84. 0% , and the average durations of feature extraction were 11.70 ms, 30. 23 ms, and 11.73 ms. The cross validation results showed that the precise local feature description method for facial expression rec- ognition is fast and accurate. Conclusion Through organ segmentation and the construction of flexible full vector triangle pat- terns, the precise local feature description method performs well in image feature description while consuming little time. The recognition results of the proposed method are better than those of the typical facial expression recognition method.