Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Journal of Cognitive Neuroscience
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Eigenspace-based face recognition: a comparative study of different approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Higher Order SVD Analysis for Dynamic Texture Synthesis
IEEE Transactions on Image Processing
Comparison of tensor unfolding variants for 2DPCA-based color facial portraits recognition
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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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.