Object recognition by indexing using neural networks

In this paper a new Distributed Neural Network Architecture (DNNA) for object recognition is presented. The proposed architecture is tested in two scenarios: occluded planar object recognition and face recognition. The DNNA is composed by several classifiers, each one with a standard ART2 Neural Network (ART2-NN) connected to a Memory Map (MM), a set of logical AND gates, an evidence register, and a set of comparators. In a first step, objects are described by a set of sub-feature vectors (SFV), during the training stage, each SFV is then fed to an ART2-NN to train it and to build its corresponding Memory Map (MM). During a second phase of indexing a new image possibly containing the object is used to retrieve from the previously constructed MM the list of candidate objects that are in the image. A selection threshold is finally used to select from this list the objects that most resemble the objects on the image.