A Weighted FMM Neural Network and Its Application to Face Detection

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.
Publisher
Springer Verlag (Germany)
Issue Date
2006-10-01
Citation

Lecture notes in computer science, v.4233, pp.177-186

ISSN
0302-9743
URI
http://hdl.handle.net/10203/330
Appears in Collection
CS-Journal Papers(저널논문)
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