Abstract
Although most of the conventional systems for object recognition have their own special targets, this paper gives a generic idea for 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 in this paper. 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. At last, the face area is detected by integrating the outputs of previous 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.
| Original language | English |
|---|---|
| Pages (from-to) | 4284-4289 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 5 |
| Publication status | Published - 1998 Dec 1 |
| Event | Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - San Diego, CA, USA Duration: 1998 Oct 11 → 1998 Oct 14 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Hardware and Architecture