Face recognition using a color PCA framework

This paper delves into the problem of face recognition using color as an important cue in improving recognition accuracy. To perform recognition of color images, we use the characteristics of a 3D color tensor to generate a subspace, which in turn can be used to recognize a new probe image. To test the accuracy of our methodology, we computed the recognition rate across two color face databases and also compared our results against a multi-class neural network model. We observe that the use of the color subspace improved recognition accuracy over the standard gray scale 2D-PCA approach [17] and the 2-layer feed forward neural network model with 15 hidden nodes. Additionally, due to the computational efficiency of this algorithm, the entire system can be deployed with a considerably short turn around time between the training and testing stages.