A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive learning of an accurate skin-color model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
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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.