Accelerating CSRN based face recognition on an NVIDIA GPGPU

Unmanned aerial vehicles (UAVs) are being equipped with high definition cameras to survey a wide range of low-contrast and diverse environments. From data captured by UAVs, image analysts can determine adversarial threats proficiently. However, there is simply too much data and not enough analysts to do this processing efficiently. Enabling computing systems to mimic the processes in the human brain to process sych data would be of significant benefit. CSRNs (cellular simultaneous recurrent networks) are capable of solving several spatial processing tasks that are carried out by human. In particular, they have been shown to be capable of pose invariant face recognition. Given the highly recurrent nature of CSRNs (a property also seen in the human cortex), the computational demands of these algorithms grow with input size. Therefore the acceleration of CSRNs would be highly beneficial. In this paper we examine the acceleration of CSRNs applied to face recognition. We develop optimized implementations of the algorithm on an Intel Xeon 2.67 GHz processor and an NVIDIA Tesla C2050 GPGPU (general purpose graphical processing unit). Our results show that the GPGPU is 22.9 times faster than the CPU implementation.

[1]  Guilherme N. DeSouza,et al.  GPU-based simulation of cellular neural networks for image processing , 2009, 2009 International Joint Conference on Neural Networks.

[2]  Robert Kozma,et al.  Beyond Feedforward Models Trained by Backpropagation: A Practical Training Tool for a More Efficient Universal Approximator , 2007, IEEE Transactions on Neural Networks.

[3]  Klaus Kofler,et al.  Performance and Scalability of GPU-Based Convolutional Neural Networks , 2010, 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing.

[4]  Shaogang Gong,et al.  Tracking and Recognition of Face Sequences , 1995 .

[5]  Yong Ren,et al.  Pose invariant face recognition using Cellular Simultaneous Recurrent Networks , 2009, 2009 International Joint Conference on Neural Networks.

[6]  P. Goldman-Rakic,et al.  Preface: Cerebral Cortex Has Come of Age , 1991 .

[7]  Paul J. Werbos,et al.  The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting , 1994 .

[8]  Kuldip K. Paliwal,et al.  Polynomial features for robust face authentication , 2002, Proceedings. International Conference on Image Processing.

[9]  X. Pang,et al.  Neural network design for J function approximation in dynamic programming , 1998, adap-org/9806001.

[10]  Nikil D. Dutt,et al.  Efficient simulation of large-scale Spiking Neural Networks using CUDA graphics processors , 2009, 2009 International Joint Conference on Neural Networks.

[11]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.