Real-time integrated face detection and recognition on embedded GPGPUs

Both face detection and face recognition have started to be used widely these days in various applications such as biometric, surveillance, security, advertisement, entertainment, and so on. The ever increasing input image size in face detection and the large input DB in face recognition keep requiring more computational power to achieve real-time processing. Recently, embedded GPUs have started to support OpenCL and many applications can be accelerated successfully as the server GPUs have. In this paper, we propose several optimization techniques for the Local Binary Pattern (LBP) based integrated face detection and recognition algorithms, and successfully accelerated them achieving 22 fps using OpenCL on ARM Mali GPU, and 38 fps using CUDA on Tegra K1 GPU for HD inputs. This corresponds to 2.9 times and 3.7 times speedups respectively. To the best of our knowledge, it is the first paper that presents the acceleration of the face detection on embedded GPGPUs, and also that presents the performance of Tegra K1 GPU.

[1]  Carlos Segura,et al.  Accelerating Boosting-Based Face Detection on GPUs , 2012, 2012 41st International Conference on Parallel Processing.

[2]  Joseph R. Cavallaro,et al.  Accelerating computer vision algorithms using OpenCL framework on the mobile GPU - A case study , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Petru Eles,et al.  General purpose computing on low-power embedded GPUs: Has it come of age? , 2013, 2013 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS).

[4]  Scott B. Baden,et al.  Accelerating Viola-Jones Face Detection to FPGA-Level Using GPUs , 2010, 2010 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines.

[5]  Kwang-Ting Cheng,et al.  Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones , 2011, Proceedings of 2011 International Symposium on VLSI Design, Automation and Test.

[6]  Xavier Martorell,et al.  Real-time GPU-based face detection in HD video sequences , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[7]  G. Clark,et al.  Reference , 2008 .

[8]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[10]  G. N. Rathna,et al.  Parallel Implementation of LBP Based Face Recognition on GPU Using OpenCL , 2012, 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies.

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Shengcai Liao,et al.  Face Detection Based on Multi-Block LBP Representation , 2007, ICB.

[13]  Amit A. Kale,et al.  Towards a robust, real-time face processing system using CUDA-enabled GPUs , 2009, 2009 International Conference on High Performance Computing (HiPC).

[14]  Bin Wang,et al.  GPU and CPU Cooperative Accelaration for Face Detection on Modern Processors , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[15]  Youngmin Yi,et al.  Fast PCA-based face recognition on GPUs , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Yousri Ouerhani,et al.  Fast face recognition approach using a graphical processing unit “GPU” , 2010, 2010 IEEE International Conference on Imaging Systems and Techniques.