Toward automation of learning: the state self-organization problem for a face recognizer

The capability of recognition is critical in learning but variation of sensory input makes recognition a very challenging task. The current technology in computer vision and pattern recognition requires humans to collect images, store images, segment images for computers and train computer recognition systems using these images. It is unlikely that such a manual labor process can meet the demands of many challenging recognition tasks that are critical for generating intelligent behavior, such as face recognition, object recognition and speech recognition. Our goal is to enable machines to learn directly from sensory input streams while interacting with the environment including human teachers. While doing so, the human teacher is not allowed to dictate the internal state value of the system. He or she can influence the system through only the system's sensors and effectors. Such a capability requires a fundamentally new way of addressing the learning problem, one that unifies learning and performance phases and requires a systematic self-organization capability. This paper concentrates on the state self-organization problem. We apply the method to autonomous face recognition.