Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Amman, Jordan.
This paper explores the possibility of using multi-core programming model that implements the Cascade correlation neural networks technique (CCNNs), to enhance the classification phase of 3D facial recognition system, after extracting robust and distinguishable features. This research provides a comprehensive summary of the 3D facial recognition systems, as well as the state-of-the- art for the Parallel Cascade Correlation Neural Networks methods (PCCNNs). Moreover, it highlights the lack of literature that combined between distributed and shared memory model which leads to novel possibility of taking advantage of the strengths of both approaches in order to construct an efficient parallel computing system for 3D facial recognition.
Al-Qatawneh, S. and Jaber, K. (2015) Parallel Cascade Correlation Neural Network Methods for 3D Facial Recognition: A Preliminary Study. Journal of Computer and Communications, 3, 54-62. doi: 10.4236/jcc.2015.35007.
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