Abstract
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.
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