Real-time nose detection and tracking based on AdaBoost and optical flow algorithms

Authors:
D. González-Ortega;F. J. Díaz-Pernas;M. Martínez-Zarzuela;M. Antón-Rodríguez;J. F. Díez-Higuera;D. Boto-Giralda
Affiliations:
Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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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.