Yi-Chu Wang
ABSTRACT
Face annotation makes it easy to share and manage digital photos and videos. While state-of-the-art face recognition algorithms can achieve high accuracy to support automatic face annotation, their implementations on an embedded platform cannot achieve real-time performance due to the demanding computational requirement. However, the availability of an embedded GPU in most smartphones offers the opportunity to use it as an accelerator for the face recognition task. In this demonstration, we show that, with acceleration achieved by the embedded low-power GPU, a real-time face annotation system could be realized on an existing off-the-shelf smartphone.
- Chu, S-W., Yeh, M-C., Cheng, K-T. 2008. A real-time, embedded face-annotation system. ACM MM Technical demonstrations Google Scholar
Digital Library
- http://opencv.willowgarage.com/wiki/FaceDetectionGoogle Scholar
- Su, Y., Shan, S., Chen, X., Gao, W. 2009. Hierarchical Ensemble of Global and Local Classifiers for Face Recognition. In IEEE Transactions on Image Processing, vol. 18, issue 8, 1885--1896 Google ScholarDigital Library
- http://developer.android.com/index.htmlGoogle Scholar
- Moreland, K., Angel E. 2003. The FFT on a GPU. In Proceeding of SIGGRAPH, pp. 112--119, July 2003 Google ScholarDigital Library
- http://www.khronos.org/openglesGoogle Scholar
Index Terms
- A GPU-accelerated face annotation system for smartphones
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