Neural network and wavelet multiresolution system for human being detection
Souad Haddadi, Christine Fernández-Maloigne · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Many applications, in robotics, require identification of human being. Using complex methods, based on model matching are too computationally expensive and not always justified. We propose a fast and simple method for identification of human being. This method takes profit of the learning capabilities of a neural network. The idea is to train a neural network on some images of persons. In order to reduce the amount of this data (images), we use wavelet multiresolution propriety analysis that allows to bring significant information content of image. This one thus is characterized by its approximation at a given resolution. After the training phase, the generalization capabilities of the network allow it to identify no-learned images. We describe here the proposed method, and we present experimental results obtained on a data base of 437 images.