A Hybrid Face Recognition Approach Using GPUMLib
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.
KeywordsGPU computing Non-Negative Matrix Factorization Multiple Back-Propagation Hybrid systems Face Recognition
- 5.Lopes, N., Ribeiro, B.: GPUMLib: a new library to combine machine learning algorithms with graphics processing units. In: 10th International Conference on Hybrid Intelligent Systems, Atlanta, USA (2010)Google Scholar
- 6.Lopes, N., Ribeiro, B.: Non-negative matrix factorization implementation using graphic processing units. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osomo, C., Yin, H. (eds.) IDEAL 2010. LNCS, vol. 6283, pp. 275–283. Springer, Heidelberg (2010)Google Scholar
- 9.Xu, B., Lu, J., Huang, G.: A constrained non-negative matrix factorization in information retrieval. In: IEEE International Conference on Information Reuse and Integration, IRI 2003, pp. 273–277 (2003)Google Scholar
- 11.Zilu, Y., Guoyi, Z.: Facial expression recognition based on NMF and SVM. International Forum on Information Technology and Applications 3, 612–615 (2009)Google Scholar