Regression techniques, such as ridge regression (RR) and logistic regression (LR), have been widely used in supervised learning for pattern classification. However, these methods mainly exploit the class label information for linear mapping function learning. They will become less effective when the number of training samples per class is small. In visual classification tasks such as face recognition, the appearance of the training sample images also conveys important discriminative information. This paper proposes a novel regression based classification model, namely Bayesian sample steered discriminative regression (BSDR), which simultaneously exploits the sample class label and the sample appearance for linear mapping function learning by virtue of the Bayesian formula. BSDR learns a linear mapping for each class to extract the image class label features, and classification can be simply done by nearest neighbor classifier. The proposed BSDR method has advantages such as small number of mappings, insensitiveness to input feature dimensionality and robustness to small sample size. Extensive experiments on several biometric databases also demonstrate the promising classification performance of our method. 
Bayesian sample steered discriminative regression for biometric image classification