Face detection from cluttered images is very challenging due to the wide variety of faces and the complexity of image backgrounds. In this paper, we propose a neural network based approach for locating frontal views of human faces in cluttered images. We use a radial basis function network (RBFN) for separation of face and non-face patterns, and the complexity of RBFN is reduced by principal component analysis (PCA). The influence of the number of hidden units and the configuration of basis functions on the detection performance was investigated. To further improve the performance, we integrate the distance from feature subspace into the RBFN. The proposed method has achieved high detection rate and low false positive rate on testing a large number of images.

The skeleton and the skeleton function of an object are important representations for shape analysis and recognition. They contain enough information to recognize an object and to reconstruct its original shape. However, they are sensitive to distortion caused by rotation and noise. This paper presents another approach for binary object representation called a modified exoskeleton(mES) that combines the previously defined exoskeleton with the use of symmetric object whose dominant property is rotation invariant. The mES is the skeleton of a circular background around the object that preserves the skeleton properties including significant information about the object for use in object recognition. Then the matching algorithm for object recognition based on the mES is presented. We applied the matching algorithm to evaluate the mES against the skeleton obtained from using 4-neighbor distance transformation on a set of artificial objects, and the experimental results reveal that the mES is more robust to distortion caused by rotation and noise than the skeleton and that the matching algorithm is capable of recognizing objects effectively regardless of their size and orientation.