Abstract
In this paper we present a fast and robust nose detection and tracking application which runs on a consumer-grade computer with video input from an inexpensive Universal Serial Bus camera. Nose detection is based on the AdaBoost algorithm with Haar-like features. A detailed study was developed to select the positive and negative training samples and the parameters of the detector. Pyramidal Lucas-Kanade optical flow tracking algorithm is applied to the nostrils from a previous nose detection in a frame of a video sequence. Tracking takes 2 ms and is robust to different face positions, backgrounds and illumination. The nose detection and tracking application can be used alone or integrated in a hand-free vision-based Human-Computer Interface.
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References
Turk, M., Kölsch, M.: Perceptual Interfaces. In: Medioni, G., Kang, S.B. (eds.) Emerging Topics in Computer Vision, pp. 456–520. Prentice Hall, Englewood Cliffs (2004)
Gorodnichy, D.O., Roth, G.: Nouse ‘Use Your Nose as a Mouse’ Perceptual Vision Technology for Hands-Free Games and Interfaces. Image and Vision Computing 22(12), 931–942 (2004)
Xu, C., Tan, T., Wang, Y., et al.: Combining Local Features for Robust Nose Location in 3D Facial Data. Pattern Recognition Letters 27(13), 1487–1494 (2006)
Zhang, L., Zhou, F., Li, W., et al.: Human-Computer Interaction System Based on Nose Tracking. In: Jacko, J.A. (ed.) HCI 2007. LNCS, vol. 4552, pp. 769–778. Springer, Heidelberg (2007)
Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Viola, P., Jones, M.J., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision 63(2), 153–161 (2005)
Stojmenovic, M.: Real Time Machine Learning Based Car Detection in Images with Fast Training. Machine Vision and Applications 17(3), 163–172 (2006)
Horn, B.K.P., Schunk, B.G.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)
Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of the Imaging Understanding Workshop, pp. 121–130 (1981)
Kim, K., Jang, D., Choi, H.: Real Time Face Tracking with Pyramidal Lucas-Kanade Feature Tracker. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part I. LNCS, vol. 4705, pp. 1074–1082. Springer, Heidelberg (2007)
The Faces96 Database, http://cswww.essex.ac.uk/mv/allfaces/faces96.html
The BioID Face Database, http://www.bioid.com/downloads/facedb
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González-Ortega, D., Díaz-Pernas, F.J., Martínez-Zarzuela, M., Antón-Rodríguez, M., Díez-Higuera, J.F., Boto-Giralda, D. (2009). Real-Time Nose Detection and Tracking Based on AdaBoost and Optical Flow Algorithms. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_18
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DOI: https://doi.org/10.1007/978-3-642-04394-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04393-2
Online ISBN: 978-3-642-04394-9
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