Feature selection method with common vector and discriminative common vector approaches

The dimension of the feature vector is very important for real time face recognition applications. High dimensional feature vectors increase the computational complexity and execution time of the face recognition system. In this work, a new feature selection method is proposed related with CVA and DCVA to reduce the dimension of the face images. Experiments are executed on two different face databases, namely AR, FERET. Great dimension reduction is achieved with slight recognition rate loss.