关键词: 信息处理技术, 人脸识别, 核映射, Fisher全局正交鉴别矢量集

Abstract: A kernel-based method to find global orthogonal discriminant vectors was proposed. This method first maps training data into a high dimensional feature space through kernel function concept in Support Vector Machine and constructs orthogonal space in the feature space. Then it maps the data from feature space into the orthogonal space and computes Fisher criterion based global orthogonal discriminants. Experiments on ORL face image datasets show that compared with current general discriminant algorithm (GDA), modified general discriminant algorithm and kernel based direct discriminant algorithm, the proposed method has a better performance in precision and robustness.

Key words: information processing, face recognition, kernel trick, Fisher global orthogonal discriminant vectors