Abstract
In this paper, a multi-stage face detection method using hybrid neural networks is presented. The method consists of three stages: preprocessing, feature extraction and pattern classification. We introduce an adaptive filtering technique which is based on a skin-color analysis using fuzzy min-max(FMM) neural networks. A modified convolutional neural network(CNN) is used to extract translation invariant feature maps for face detection. We present an extended version of fuzzy min-max (FMM) neural network which can be used not only for feature analysis but also for pattern classification. Two kinds of relevance factors between features and pattern classes are defined to analyze the saliency of features. These measures can be utilized to select more relevant features for the skin-color filtering process as well as the face detection process.
This research was supported as a 21st Century Frontier R&D Program and Brain Neuroinformatics Research Program sponsored by Ministry of Information and Communication and Minister of Commerce, Industry and Energy in Korea.
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Kim, HJ., Lee, J., Yang, HS. (2006). Robust Real-Time Face Detection Using Hybrid Neural Networks. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_76
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DOI: https://doi.org/10.1007/11816102_76
Publisher Name: Springer, Berlin, Heidelberg
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