关键词: 局部特征, 非局部特征, 独立分量分析, 无监督投影鉴别, 特征抽取, 人脸识别

Abstract: Enhanced Independent Component Analysis（EICA） is an unsupervised feature extraction method which is presented based on the overall characteristics，so EICA doesn’t fit to solve such a nonlinear problem as face recognition.Unsupervised Discriminant Projection（UDP） technique is developed for dimensionality reduction of high-dimensional data，and it considers both the local characteristics and non-local characteristics，thus UDP is effective for classification of samples.In this paper，an enhanced unsupervised method is introduced，which has advantages of both EICA and UDP as：（1）it can reflect high-order statistics of samples（2）it is able to discover essential data structure，and obtain a set of effective discriminant projection axis for classification.The experiments on the Yale and FERET databases validate the effectiveness of the proposed method.

Key words: local characteristic, global characteristic, Independent Component Analysis（ICA）, Unsupervised Discriminant Projection (UDP), feature extraction, face recognition