Neural, Parallel & Scientific Computations
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Neural Networks: Computational Models and Applications (Studies in Computational Intelligence)
Neural Networks: Computational Models and Applications (Studies in Computational Intelligence)
Facial Expression Recognition Based on NMF and SVM
IFITA '09 Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 03
Using underapproximations for sparse nonnegative matrix factorization
Pattern Recognition
An adaptively weighted sub-pattern locality preserving projection for face recognition
Journal of Network and Computer Applications
GPU implementation of the multiple back-propagation algorithm
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Non-negative matrix factorization implementation using graphic processing units
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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We present a hybrid face recognition approach which relies on a Graphics Processing Unit (GPU) Machine Learning (ML) Library (GPUMLib). The library includes a high-performance implementation of the Non-Negative Matrix Factorization (NMF) and the Multiple Back-Propagation (MBP) algorithms. Both algorithms are combined in order to obtain a reliable face recognition classifier. The proposed approach first applies an histogram equalization to the original face images in order to reduce the influence from the surrounding illumination. The NMF algorithm is then applied to reduce the data dimensionality, while preserving the information of the most relevant features. The obtained decomposition is further used to rebuild accurate approximations of the original data (by using additive combinations of the parts-based matrix). Finally, the MBP algorithm is used to build a neural classifier with great potential to construct a generalized solution. Our approach is tested in the Yale face database, yielding an accuracy of 93.33% thus demonstrating its potential. Moreover, the speedups obtained with the GPU greatly enhance real-time implementation face recognition systems.