提出了一种基于分块独立分量分析(BICA)的特征提取方法。该方法通过将人脸分块降低了光照条件、人脸表情等外在因素对人脸识别的影响，并先后将分块后重组的矩阵的行和列作为训练样本提取独立分量，由于训练样本维数很小，因此它降低了传统独立分量分析（ICA）方法中存在的高维小样本问题产生的识别错误率，同时减少了识别时间。在Yale人脸库和AR人脸库上验证了该算法的有效性。

A novel feature extraction method using block-based independent component analysis (BICA) is proposed in this paper. BICA partitions the facial image into a few blocks, reducing the influence of some factors such as lighting condition and facial expression on face recognition. The method takes the row and the column vector of the reconstructed matrix as the training vector sequentially to extract independent components. Since the dimensionality of the training vector in Block-ICA is much smaller than that in the traditional ICA, it can reduce the face recognition error caused by the dilemma in traditional ICA, i.e. the number of available training samples are great less than thoes of the training vector, and thus reduce the recognition time. Experiments on the Yale and AR databases validate the effectiveness of the proposed method.