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中文摘要：
特征提取是模式识别研究领域的一个热点．为了更好地解决人脸识别中的特征提取问题，定义了一种新的基于Fisher鉴别极小准则的特征提取方法，并且提出了类间散布矩阵零空间的概念，解决了先前Fisher线性变换方法中的最终特征维数受类别数的限制．在人脸数据库上的实验结果验证了该算法的有效性．
英文摘要：
Feature extraction is one of the hot topics in the field of pattern recognition. In this paper, a new feature extraction method called Fisher discriminant minimal criterion is proposed to improve the performance of feature extraction. Conventional Fisher discriminant criterion is inversed and null space of between-class scatter matrix is defined in this algorithm. Therefore, limitation of final eigenvectors＇ dimensions determined by class number is overcome and more effective classification information can be achieved. Experimental results on face databases demonstrate the effectiveness of the proposed algorithm.
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