Learning Discriminative Features Based on Distribution

In this paper, a novel feature named adaptive projection LBP (APLBP) is proposed for face detection. To promote discriminative power, the distribution information of training samples is embedded into the proposed feature. APLBP is generated by LDA which maximizes the margin between positive and negative samples adaptively, utilizing characteristics of similarity to Gaussian distribution of the training samples. Asymmetric Gentle Adaboost is utilized to train strong classifier and nested cascade is applied to construct the final detector. Experimental results based on MIT+CMU database demonstrate that APLBP feature outperforms several well-existing features due to its excellent discriminative power with less feature number.