Abstract
Finding discriminative feature is crucial for building a high-performance object detection system, which has an effect on the detection speed and accuracy. In this paper, we propose a novel discriminative weighted pooling feature based on the multiple channel maps for multi-view face detection. The color and shape statistics of face structure can be utilized to enhance the discriminative ability of the box filter, which is generalized from the square channel filter. The discriminative information can be obtained with LDA and imbalance embedding LDA method, which is superior to the baseline box filter. The experimental result on the FDDB dataset shows that our proposed method has some advantages in accuracy or speed when compared with many other state-of-the-art methods.
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Acknowledgments
This project is supported by the NSF of China (61305058, 61473086), the NSF of Jiangsu Province (Grants No. BK20140566, BK20150470, BK20130471), the Fundamental Research Funds for the Jiangsu University (13JDG093), the NSF of the Jiangsu Higher Education Institutes of China (15KJB520008), and China Postdoctoral science Foundation (2014M561586).
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Shi, S., Shen, J., Zuo, X., Yang, W. (2016). A Novel Discriminative Weighted Pooling Feature for Multi-view Face Detection. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_37
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DOI: https://doi.org/10.1007/978-981-10-3002-4_37
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