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

Authors: Noel Lopes; Bernardete Ribeiro

Addresses: School of Management and Technology, Polytechnic Institute of Guarda, 6300-559 Guarda, Portugal. ' CISUC, Department of Informatics Engineering, University of Coimbra, 3030-290 Coimbra, Portugal

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.

Keywords: non-negative matrix factorisation; NMF; multiple back-propagation; dimensionality reduction; pattern recognition; face recognition; GPU computing; graphics processing unit; hybrid approaches; neural approaches; high-performance units; non-linear algorithms; unsupervised algorithms; data reconstruction; original data; data projection; lower dimensional spaces; noise reduction; information removal; worthless information; generalisation potential; parts-based capabilities; representation capabilities; Yale University; universities; higher education; USA; United States; facial images databases; Olivetti Research Laboratory; ORL; American Telephone and Telegraph Company; AT&T; robustness; lighting conditions; speedups; real-time implementation; real-world applications; data mining; data modelling; data management; intelligent data analysis.

DOI: 10.1504/IJDMMM.2012.046807

International Journal of Data Mining, Modelling and Management, 2012 Vol.4 No.2, pp.138 - 155

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 09 May 2012 *

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