We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality red

人脸表情识别是模式识别领域中一个重要的研究方向。人脸表情识别包括：从背景图像中检测人脸、图像采集、对人脸图像进行预处理、PCA提取表情特征、表情识别。-Face recognition is an important research direction in the field of pattern recognition. Face recognition is made up of the following parts: face detection from the backgrou

基于PCA的人脸表情识别，可以辨别高兴，愤怒和厌恶三种表情。 -In this project, Eigenfaces are used to classify facial expression. It has been assumed that, facial expression can be classified into some discreet classes (like anger, happiness, disgust or sadness) whereas: 1. A

Matlab表情识别，特征脸[1 ]作为面部表情分类的方法。首先，利用训练图像创建低维人脸空间（pca）。这是通过训练图像集主成分分析（PCA）及图片主成分分析（即具有较大特征值的特征向量）获得的。 结果，所有的测试图像以所选择的主成分表示，计算投影图像与所有投影列车图像的欧几里得距离，选择最小值以找出与试验图像最相似的训练图像。(The feature face [1] is used as a facial expression classification method. Firstly,