Towards a hybrid NMF-based neural approach for face recognition on GPUs

Abstract

We present a hybrid face recognition approach that relies on a high-performance graphics processing unit (GPU) implementation of the non-negative matrix factorisation (NMF) and multiple back-propagation (MBP) algorithms. NMF is a non-linear unsupervised algorithm which reduces the data dimensionality, while preserving the information of the most relevant features allowing for the reconstruction of the original data. The projection of the data on lower dimensional spaces accounts for noise reduction and enables to remove worthless information. By combining the strengths of both algorithms, we are able to take advantage of the high generalisation potential of MBP, while upholding the parts-based representation capabilities provided by the NMF algorithm. The proposed approach is tested on the Yale and AT&T (ORL) facial images databases, evidencing robustness with different lighting conditions, thus demonstrating its potential and usefulness. Moreover, the speedups obtained with the GPU greatly enhance real-time implementation face recognition systems and may be crucial for many real-world applications.