Random Feature Discriminant for Linear Representation Based Robust Face Recognition

The linear representation based classification methods include two independent steps: representation and decision. First, the query image is represented as a linear combination of training samples. Then the classification decision is made by evaluating which class leads to the minimum class-wise representation error. However, these two steps have different goals. The representation step prefers accuracy while the decision step requires discrimination. Thus precisely representing the query image does not always benefit the decision process. In this paper, we propose a novel linear representation based classifier which no longer separates representation from decision. We repeatedly construct linear representation based classification models with randomly selected features. Then the best model is selected by using the representation discriminant criterion (RDC) which evaluates the discrimination of a representation model. We conduct extensive experiments on public benchmark databases to verify the efficacy of the proposed method.