Abstract
In this paper, we introduce a modified fuzzy min-max(FMM) neural network model for pattern classification, and present a real-time face detection method using the proposed model. The learning process of the FMM model consists of three sub-processes: hyperbox creation, expansion and contraction processes. During the learning process, the feature distribution and frequency data are utilized to compensate the hyperbox distortion which may be caused by eliminating the overlapping area of hyperboxes in the contraction process. We present a multi-stage face detection method which is composed of two stages: feature extraction stage and classification stage. The feature extraction module employs a convolutional neural network (CNN) with a Gabor transform layer to extract successively larger features in a hierarchical set of layers. The proposed FMM model is used for the pattern classification stage. Moreover, the model is utilized to select effective feature sets for the skin-color filter of the system.
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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References
Simpson, P.K.: Fuzzy Min-Max Neural Networks Part 1: Classification. IEEE Transaction on Neural Networks 3(5), 776–786 (1997)
Gabrys, B., Bargiela, A.: General Fuzzy Min-Max Neural Network for Clustering and Classification. IEEE Transaction on Neural Networks 11(3), 769–783 (2000)
Feraud, R., Bernier, O.J., Viallet, J.E., Collobert, M.: A Fast and Accurate Face Detector Based on Neural Networks. IEEE Transaction on Pattern Analysis and Machine Intelligence 23(1), 42–53 (2001)
Garcia, C., Delakis, M.: Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(11), 1408–1423 (2004)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face Detection: A Convolutional Neural-Network Approach. IEEE Transaction n Neural Networks 8(1), 98–113 (1997)
Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face Detection in Color Images. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)
Storring, M., Kocka, T., Andersen, H.J., Granum, E.: Tracking Regions of Human Skin Through Illumination Changes. Pattern Recognition Letters 24, 1715–1723 (2003)
Zhu, Q., Cheng, K.T., Wu, C.T., Wu, Y.L.: Adaptive Learning of an Accurate Skin-Color Model. In: Proceeding of the Sixth IEEE International Conf. on Automatic Face and Gesture Recognition, vol. 1 (2004)
Kim, H.-J., Yang, H.-S.: A Modified FMM Neural Network for Pattern Classification and Feature Analysis. In: Proceeding of the ISATED International Conference on Artificial Intelligence and Applications, vol. 1 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, HJ., Lee, J., Yang, HS. (2006). A Weighted FMM Neural Network and Its Application to Face Detection. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_20
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DOI: https://doi.org/10.1007/11893257_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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