Abstract
Face detection has been considered one of the most important areas of research in computer vision due to its wide range of use in human face-related applications. This paper addresses the problem of face detection using Hough transform employed within the random forests framework. The proposed Hough forests-based method is a task-adapted codebooks of local facial appearance with a randomized selection of features at each split that allow fast supervised training and fast matching at test time, where the codebooks are built upon a pool of heterogeneous local appearance features and the codebook is learned for the face appearance features that models the spatial distribution and appearance of facial parts of the human face. Experimental results are included to verify the effectiveness and feasibility of the proposed method.
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Hassaballah, M., Ahmed, M. (2014). A Random Decision Forests Approach to Face Detection. In: Zhang, Y.J., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2014. Lecture Notes in Computer Science, vol 8641. Springer, Cham. https://doi.org/10.1007/978-3-319-09994-1_37
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DOI: https://doi.org/10.1007/978-3-319-09994-1_37
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