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.