Pixel-Based Hierarchical-Feature face detection
- 1 January 2010
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing
- p. 1638-1641
- https://doi.org/10.1109/icassp.2010.5495533
Abstract
In this paper, the Pixel-Based Hierarchical-Feature Adaboosting (PBHFA) method is presented. The purpose of this approach is the reduction of computation complexity in face-detection tasks. The Adaboosting method has attracted attention for its efficient face-detection performance. However, in the training process, the large number of possible Haar-like features in a standard sub-window becomes time consuming, which makes specific environment feature adaptation extremely difficult. For this object, the PBHFA is proposed as a possible solution. Given a M × N sub-window, the number of possible PBH features is simplified down to a level less than M × N, which significantly reduces the length of the training period by a factor of 1500. Moreover, when the trained PBH features are employed for practical face-detection tasks, the hierarchically structural pattern matching also has lower complexity than that of the integral-image based approach in the traditional Adaboosting method. As documented in experimental results, with the MIT-CMU profile test set are examined, the proposed PBH features have shown significantly more effective than Haar-like features.Keywords
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