Abstract
The development of web and digital camera nowadays has made it easier to collect more than hundreds of thousands of examples. How to train a face detector based on the collected enormous face database? This paper presents a manifold-based method to subsample. That is, we learn the manifold from the collected face database and then subsample training set by the estimated geodesic distance which is calculated during the manifold learning. Using the subsampled training set based on the manifold, we train an AdaBoost-based face detector. The trained detector is tested on the MIT+CMU frontal face test set. The experimental results show that the proposed method is effective and efficient to train a classifier confronted with the huge database.
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Chen, J., Wang, R., Yan, S., Shan, S., Chen, X., Gao, W. (2005). How to Train a Classifier Based on the Huge Face Database?. In: Zhao, W., Gong, S., Tang, X. (eds) Analysis and Modelling of Faces and Gestures. AMFG 2005. Lecture Notes in Computer Science, vol 3723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564386_8
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DOI: https://doi.org/10.1007/11564386_8
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