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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Thomas, M., Kumar, S., & Kambhamettu, C. (2008). Face recognition using a color PCA framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5008 LNCS, pp. 373–382). https://doi.org/10.1007/978-3-540-79547-6_36
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