TITLE:
Parallel Cascade Correlation Neural Network Methods for 3D Facial Recognition: A Preliminary Study
AUTHORS:
Sokyna M. Al-Qatawneh, Khalid Mohammad Jaber
KEYWORDS:
Parallel Computing, CCNNs, 3D Facial Recognition, MPI, GPGPU, Multicore
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.5,
May
25,
2015
ABSTRACT: 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.