Face Recognition Using a Color PCA Framework

This paper delves into the problem of face recognition using color as an important cue in improving the accuracy of recognition. To perform recognition of color images, we use the characteristics if a 3D color tensor so as to generate a subspace that can be used to recognize a new probe image. To test the accuracy of our methodology, we have performed both qualitative and quantitative experiments. In the quanti- tative measurements, we computed the recognition rate across two color face databases. For the qualitative measurements, we perform face recog- nition using a low cost web camera. We observe that the use of the color subspace improves recognition accuracy over the standard gray scale 2D- PCA approach [20]. Additionally, due to computational efficiency of the algorithm, the entire system can be deployed with a short turn around time between the training and testing stages.