Face detection using an adaptive skin-color filter and FMM neural networks

Authors:
Ho-Joon Kim;Tae-Wan Ryu;Juho Lee;Hyun-Seung Yang
Affiliations:
School of Computer Science and Electronic Engineering, Handong University, Pohang, Korea;Department of Computer Science, California State University, Fullerton, CA;Department of Computer Science, KAIST, Daejeon, Korea;Department of Computer Science, KAIST, Daejeon, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 6
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Abstract

In this paper, we present a real-time face detection method based on hybrid neural networks. We propose a modified version of fuzzy min-max (FMM) neural network for feature analysis and face classification. A relevance factor between features and pattern classes is defined to analyze the saliency of features. The measure can be utilized for the feature selection to construct an adaptive skin-color filter. 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. In this paper we first describe the behavior of the proposed FMM model, and then introduce the feature analysis technique for skin-color filter and pattern classifier.