Hong Pan and Yaping Zhu and Liang Zheng Xia
Hierarchical PSOAdaboost Based Classifiers for Fast and Robust Face Detection
1322 - 1327
2011
5
11
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/101
https://publications.waset.org/vol/59
World Academy of Science, Engineering and Technology
We propose a fast and robust hierarchical face detection system which finds and localizes face images with a cascade of classifiers. Three modules contribute to the efficiency of our detector. First, heterogeneous feature descriptors are exploited to enrich feature types and feature numbers for face representation. Second, a PSOAdaboost algorithm is proposed to efficiently select discriminative features from a large pool of available features and reinforce them into the final ensemble classifier. Compared with the standard exhaustive Adaboost for feature selection, the new PSOAdaboost algorithm reduces the training time up to 20 times. Finally, a threestage hierarchical classifier framework is developed for rapid background removal. In particular, candidate face regions are detected more quickly by using a large size window in the first stage. Nonlinear SVM classifiers are used instead of decision stump functions in the last stage to remove those remaining complex nonface patterns that can not be rejected in the previous two stages. Experimental results show our detector achieves superior performance on the CMUMIT frontal face dataset.
Open Science Index 59, 2011