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Xu, C.H., Wang, Y.H., Tan, T.N. and Quan, L. (2004) Depth vs. Intensity: Which Is More Important for Face Recognition. 17th International Conference on Pattern Recognition, 1, 342-345.

has been cited by the following article:

• 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.