A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition

Parallel pattern recognition requires great computational resources; it is NP-complete. From an engineering point of view it is desirable to achieve good performance with limited resources. For this purpose, we develop a serial model for visual pattern recognition based on the primate selective attention mechanism. The idea in selective attention is that not all parts of an image give us information. If we can attend only to the relevant parts, we can recognize the image more quickly and using less resources. We simulate the primitive, bottom-up attentive level of the human visual system with a saliency scheme and the more complex, top-down, temporally sequential associative level with observable Markov models. In between, there is a neural network that analyses image parts and generates posterior probabilities as observations to the Markov model. We test our model first on a handwritten numeral recognition problem and then apply it to a more complex face recognition problem. Our results indicate the promise of this approach in complicated vision applications.