Abstract—When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best performance for a face recognition system. We propose a new regularization scheme. The proposed method is evaluated using the Olivetti Research Laboratory database, the Yale database, and the Feret database. Index Terms—Face recognition, optimization, regularized discriminant analysis (RDA), small sample-size problem. I.

This paper presents a method for rotation free online handwritten Chinese character recognition (RFOHCCR). Given a skew online handwritten character sample, two orientation correction steps, including angle rectification according to the starting point, angle readjustment based on principal direction axes, are first performed to rectify the skew angle of the sample. Then 8-directional feature is extracted and the character is classified using the classifier trained by artificially rotated samples. Experiments on 863 online Chinese character dataset and SCUT-COUCH dataset show the effectiveness of the proposed approach. 1.