Affiliation:
1. Northeastern University
Abstract
In order to do further research on face recognition, this paper constructs system software work environment on the hardware platform, and then AdaBoost algorithm is given and transplanted into this system. According to the detection speed of the system and the detection rate, this paper does simulation results, it shows that the speed of each frame image detected by the system is about 110 to 130 milliseconds, and the detection rate of face rotation of small range is 85% or more, which shows the system can meet the practical needs and has widely application.
Publisher
Trans Tech Publications, Ltd.
Reference7 articles.
1. Chellappa R, Wilson C L, Sirohey S. Human and machine recognition of faces: A survey[J], Proceedings of the IEEE, 1995, 83(5) 705-740.
2. J. -K. Zhang and W. -K. Ma, Full diversity blind Alamouti space-time block codes for unique identification of flat-fading channels, IEEE Trans. Signal Process, 2009, 57(2)635-644.
3. Yang Ming-Hsuan, David J K, Narendra A. Detecting Faces in Images: A Survey [J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(1)34-58.
4. Fu Z L, Zhao X H, Miao Q, et al. Ensemble Learning Algorithms: Generalization of AdaBoost, Journal of Sichuan University (Engineering science edition), 2010, 42(6) 91-98.
5. H. Ito, K. Yoshino, Y. Muramoto, H. Yamamoto, and T. Ishibashi, Sub-terahertz transceiver module integrating uni-traveling-carrier photodiode, Schottky barrier diode, and planar circulator circuit, J. Lightw. Technol, 2010, 28(24) 3599–3605.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献