目前已存在很多基于统计的人脸整体识别方法，独立元分析方法就是一种基于信号高阶统计特性的方法。但由于人脸光照、姿态、信息缺损等外部不可避免因素会引起整个人脸灰度图像产生很大的变化，因而会对这类整体统计性方法的稳定性产生很大影响。为此提出了一种基于独立元分析和最近邻特征线的局部人脸识别方法。首先，通过对人眼的手工定位并依据人脸几何特征完成对人脸图像的截取和局部分块，从而移除发型等无用信息；然后对每个局部图像进行PCA／ICA特征提取；最后的识别阶段，通过最近邻特征线方法得到各自识别距离，并通过对各部分设置合理的权重来综合判定。实验结果表明，作为一种有效的识别方法，分块独立元方法在识别率、识别的稳定性、应用的灵活性等方面都优于传统的整体识别方法。

A number of current face recognition algorithms use whole face representations found by statistical methods. Independent Component Analysis(ICA) is an example of such methods which is based on signal high-order statistic characteristics. While such unavoidable external factors as illumination, posture and information deformity will cause great changes of gray-scale image data, and eventually will decrease the stability of recognition. This paper presents a local face recognition algorithm that is based on ICA and the nearest feature line (NFL). Firstly, by using manually aligned eye position, segmenting a face image into two parts according to the geometric characteristics of human face, removing hair style and other useless information, then processing principal component analysis (PCA) and ICA for respective parts, and calculating corresponding NFL distance, ultimately processing comprehensive recognition by setting reasonable coefficient of weight. Compared with traditional holistic image representation, this method has many advantages, such as a much higher recognition rate, more stable and flexible in practice. Through a number of experiments, it proves to be an efficient human face recognition algorithm.