An Effective Method to Reduce False Alarms in Video Face Detection

Abstract:

This paper briefly introduced the development of video face detection and point out the shortage of current face detection system that may produce much of false alarms. Then we detail the classic Viola face detector which using integral image, Haar-like features and AdaBoost algorithm for training. Compared with Viola face detector, we proposed an available multi-model fusion method to reduce false alarms in video face detection that is combining head-shoulder detector with HOG features. After introduced the related knowledge of HOG features, we proposed a fusion detector structure which can improve the accuracy and efficiency of detection.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 3619-3623
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.3619
• Online since: August 2013
• Authors: Bing Li, Yuan Yan Tang, Di Wen, Zhen Chao Zhang, Bo Yang Ding
• Keywords: False Alarms, Fusion Detector Structure, Haar-Like Features, HOG Features

References

[1] P. Viola, and M. Jones, Robust real-time face detection, International Journal of Computer Vision, 57(2): 137–154, May (2004).

DOI: 10.1023/b:visi.0000013087.49260.fb

[2] C. Papageorgiou, M. Oren and T. Poggio, A general framework for object detection, In Proceedings of the 6th International Conference on Computer Vision (ICCV), pages 555-562, January (1998).

[3] Y. Freund, and R. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, 55(1): 119-139, August (1997).

DOI: 10.1006/jcss.1997.1504

[4] N. Dalal, and B. Triggs, Histograms of oriented gradients for human detection, In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pages 886-893, June (2005).

DOI: 10.1109/cvpr.2005.177

[5] Q. Zhu, S. Avidan and M. C. Yeh, Fast human detection using a cascade of histograms of oriented gradients, In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), pages 1491—1498, (2006).

DOI: 10.1109/cvpr.2006.119

[6] B. Wu, and R. Nevatia, Tracking of multiple humans in meetings, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2006 (CVPRW 2006), pages 143-143, New York, June (2006).

DOI: 10.1109/cvprw.2006.205

[7] P. Sabzmeydani, and G. Mori, Detecting pedestrians by learning shapelet features, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007 (CVPR 2007), pages 1-8, Minneapolis, June (2007).

DOI: 10.1109/cvpr.2007.383134

[8] P. Dollar, C. Wojek and B. Schiele, Pedestrian detection: A benchmark, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009 (CVPR 2009), pages 304-311, Miami, June (2009).

DOI: 10.1109/cvpr.2009.5206631