基于独立分量分析的人脸自动识别方法研究

提出了一种独立分量分析（ICA）和遗传算法（GA）相结合的人脸自动识别方法，人脸图像的独立的基的获取是采用基于四阶统计信息的ICA算法；为了减少计算复杂度，对原因像进行滤波降维，并用遗传算法对ICA求得的独立基集合进行搜索得到了一个最优的独立基子集，最后，选择合适的分类器根据待识别图像在独立基上投影系数进行分类判决，对人脸图像库的实验表明本方法识别率比基于主元分析 特征脸方法高，且计算量小于传统的基于ICA人脸识别方法。

RESEARCH OF AUTOMATIC FACE RECOGNITION BASED ON ICA

A combined method for automatic face recognition with both independent component analysis (ICA) and genetic algorithms (GA) was proposed. The fourth order blind identification (FOBI) algorithm was used to derive the independent sources out of the face images. To decrease the computing complexity, the dimension of the original image was reduced and GA was applied to the sources set to get an optimal subset of it. Faces can be recognized by classifying the coefficients of the face image projecting to the independent bases. The experiments in the face database show both a higher recognition rate than the eigenface method based on PCA and a lower computing complexity than the traditional ICA face recognition method.