This paper describes a Hough Forest based approach for fast head pose estimation in RGB images. The system has been designed for Human-Computer Interaction (HCI), in a way that with just a simple web-cam, our solution is able to detect the head and simultaneously estimate its pose. We leverage the Hough Forest with Probabilistic Locally Enhanced Voting model, and integrate it into a system with a skin detection step and a tracking filter for the head orientation. Our implementation drastically speeds up the head pose estimations, improving their accuracy with respect to the original model. We present extensive experiments on a publicly available and challenging dataset, where our approach outperforms the state-of-the-art.
CITATION STYLE
García-Montero, M., Redondo-Cabrera, C., López-Sastre, R., & Tuytelaars, T. (2015). Fast head pose estimation for human-computer interaction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9117, pp. 101–110). Springer Verlag. https://doi.org/10.1007/978-3-319-19390-8_12
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