A visual nervous system based multi-module neural network for object recognition

Although most of the conventional systems for object recognition have their own special targets, this paper gives a generic idea for a universal object recognition method. The proposed multi-module neural network (MMNN) is a hierarchical network with cascade connections, and consists of several modules which can detect specific features. MMNN is constructed based on the information processing of the visual nervous system such as a column structure in the Visual Area I and the hierarchical hypothesis of Hubel-Wiesel. As an example of a target object, we deal with human faces detection. This system consists of several modules in parallel which are trained to respond selectively to human face components: the eyes, the nose, and the mouth. Finally, the face area is detected by integrating the outputs of previous a cell layer. We carried out a lot of experiments using 100 images having complex background to conform the effectiveness of the proposed scheme. 83% of faces are detected correctly