A Weighted FMM Neural Network and Its Application to Face Detection

  • Ho-Joon Kim
  • Juho Lee
  • Hyun-Seung Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


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.


False Alarm Rate Face Detection Convolutional Neural Network Feature Range Contraction Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ho-Joon Kim
    • 1
  • Juho Lee
    • 2
  • Hyun-Seung Yang
    • 2
  1. 1.School of Computer Science and Electronic EngineeringHandong UniversityPohangKorea
  2. 2.Department of Computer ScienceKAISTDaejeonKorea

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