Face Feature Eztraction Based on Mazimum Discriminant Information Projection
In face recognition, feature extraction techniques are widely employed to reduce the dimensionality of data. In this paper, we introduce a new feature extraction algorithm, called Maximum Discriminant Information Projection(MDIP). MDIP considers both the between-class scatter and the within-class scatter in the processing of manifold learning. Equivalently, the goal of MDIP is to preserve the intrinsic graph characterizes the interclass compactness and connects each data point with its neighboring points of the same class. Different from Principal component analysis(PCA)that aims to find a linear mapping which preserves total variance by maximizing the trace of feature variance. While locality preserving projections(LPP) that is in favor of preserving the neighborhood structure of the data set We choose proper distance metric to assess the between and within-class scatter. Extensive experiments on face recognition demonstrate that the new feature extractors are effective, stable and efficient.
Kezheng Lin Sheng Lin Huixin Wang
国际会议
武汉
英文
136-140
2008-12-19（万方平台首次上网日期，不代表论文的发表时间）