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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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
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