A feature extraction approach based on typical samples and its application to face recognition

To overcome shortcomings of traditional linear discriminant analysis such as failing to extract features of data with complex distributions, a new LDA approach is proposed in this paper. This approach is based on the following perspective: for a sample, the sample that is from the same class and is the farthest away from this sample, is typical intra-class sample of this sample. On the other hand, for the same sample, the nearest neighbor from each of other classes is called typical inter-class sample. In practice "typical samples" of a sample have indicative meaning for the space relation between this sample and the others. The new LDA approach bases definitions of between-class and within-class scatter matrices on these typical samples. As a result, the linear transform associated with our approach is able to maximize the distances between a sample and the corresponding typical inter-class samples, while minimizing the distance between the same sample and the typical intra-class sample. The proposed new approach is able to extract features of not only data with simple distributions but also the data with complex distributions, which means that the new LDA approach has wider applicability than traditional LDA.