Abstract: Aiming at the problem that the face recognition system is vulnerable to the change of gesture, expression and occlusion in a non-controlled environment, a feature extraction method based on Geodesic Mapping Analysis（GMA） is proposed. Firstly, the similarity by calculating the geodetic distance between two pixels is measured. Then, the kernel sparse description model is built for the extracted GMA features, and the classification and recognition of the features are implemented in the nonlinear space. Experiments on the ORL and Yale-B face databases show that this method has a higher recognition rate in respect of free-form face images that deal with the change of severe occlusion, gesture and expression, which greatly improves the ability of face recognition system to deal with real complexed environment.

Key words: face recognition, Geodesic Mapping Analysis（GMA）, kernel sparse description, nonlinear space

摘要： 针对人脸识别系统在非控制环境下易受姿态、表情和遮挡变化影响的问题，提出了一种基于测地映射分析（Geodesic Mapping Analysis，GMA）的特征提取方法。通过计算两个像素点间的测地距离来度量相似性，对提取的GMA特征进行核稀疏描述建模，并在非线性空间中实现特征的分类识别。在ORL和Yale-B人脸数据库上的实验表明，该方法在应对重度遮挡、姿态和表情变化的自由形式人脸图像方面具有更高的识别率，大大提高了人脸识别系统应对真实复杂环境的能力。

关键词: 人脸识别, 测地映射分析（GMA）, 核稀疏描述, 非线性空间