Complexity Reduced Face Detection Using Probability-Based Face Mask Prefiltering and Pixel-Based Hierarchical-Feature Adaboosting

The Adaboosting 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. This lette...

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Bibliographic Details
Published in:IEEE signal processing letters 2011-08, Vol.18 (8), p.447-450
Main Authors: Guo, Jing-Ming, Lin, Chen-Chi, Wu, Min-Feng, Chang, Che-Hao, Lee, Hua
Format: Article
Language:English
Subjects:
Adaboost
Classification algorithms
Face
Face detection
Feature extraction
hierarchical feature
Histograms
Pixel
probability-based face mask
Training
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Summary:The Adaboosting 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. This letter presents a two-stage hybrid face detection scheme using Probability-based Face Mask Pre-Filtering (PFMPF) and the Pixel-Based Hierarchical-Feature Adaboosting (PBHFA) method to effectively solve the above-mentioned problems in cascade Ad aboosting. The two stages both provide far less training time than that of the cascade Adaboosting and thus reduce the computation complexity in face-detection tasks. In particular, the proposed PFMPF can effectively filter out more than 85% nonface in an image and the remaining few face candidates are then secondly filtered with a single PBHF Adaboost strong classifier. 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 two-stage hybrid face detection scheme are employed for practical face-detection tasks, the complexity is still lower than that of the integral-image based approach in the traditional Adaboosting method. Experimental results obtained using the gray feret database show that the proposed two-stage hybrid face detection scheme is significantly more effective than Haar-like features.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2011.2146772