TY - GEN
T1 - Real-time integrated face detection and recognition on embedded GPGPUs
AU - Yi, Saehanseul
AU - Yoon, Illo
AU - Oh, Chanyoung
AU - Yi, Youngmin
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/18
Y1 - 2014/11/18
N2 - 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.
AB - 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.
KW - Embedded GPGPU
KW - Face Detection
KW - Face Recognition
KW - Mali
KW - Tegra K1
UR - http://www.scopus.com/inward/record.url?scp=84919422026&partnerID=8YFLogxK
U2 - 10.1109/ESTIMedia.2014.6962350
DO - 10.1109/ESTIMedia.2014.6962350
M3 - Conference contribution
AN - SCOPUS:84919422026
T3 - 2014 IEEE 12th Symposium on Embedded Systems for Real-Time Multimedia, ESTIMedia 2014
SP - 98
EP - 107
BT - 2014 IEEE 12th Symposium on Embedded Systems for Real-Time Multimedia, ESTIMedia 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Symposium on Embedded Systems for Real-Time Multimedia, ESTIMedia 2014
Y2 - 16 October 2014 through 17 October 2014
ER -