ICA/NFL Local Face Recognition
基于ICA和NFL分类的局部人脸识别方法

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.