9617121 Javidi Artificial neural networks mimic biological neural systems and are characterized by massively interconnected processing elements called neurons. The processing is performed by training and adjusting the strength of the interconnection between neurons. Neural network systems are ideal for solving problems that are difficult to describe mathematically. Even though neural networks hold a great promise for solving complex computational problems, they have not been utilized extensively in day-to-day applications, and their application has been limited mainly to military projects. Widespread commercialization of neural network would greatly benefit the field and would make available more funds for R&D. Development of a practical, low-cost optoelectronic neural system that would take advantage of the computational potential of neural networks would facilitate further commercialization of both neural network and optoelectronic technology. Recent research has discovered new hardware and software for an application for face recognition and other biometrics that is both uniquely suited to exploit the characteristics of neural processing and has the potential for widespread commercialization. This proposal aims to develop a compact, low-cost optoelectronic processor to implement a neural system for face recognition. Face recognition and classification is a difficult task because the facial appearance is constantly changing due to different head perspectives, facial expressions, different illuminations, hair styles, etc. The face recognition problem requires storing a large data base of facial features to successfully implement the massive interconnection between the neurons required to classify facial images. The proposed system can use optical recording materials such as photopolymers to store the large facial data base for an individual on an ID card, such as a driver's license. For inspection, a live image of the person carrying the card is displayed on an optoelectronic device and is compared to the facial features stored in the photopolymer film on the card. This comparison is performed by an optoelectronic neural chip which carries out the computation necessary for an accurate classification. For additional security, the facial features can be optically encrypted by phase encoding to prevent reproduction of the ID cards by unauthorized people 5 . One of the advantages of the proposed system is that using optical materials, large amounts of facial information can be stored on a relatively small area, fitting easily on the card. Also, the facial information is read out in parallel by light beams which can perform parallel computation on large arrays of data, and therefore the recognition can be performed in a short period of time. Neural algorithms will be developed to provide reliable face recognition methods. Challenges are in the area of producing very low probability of error algorithms, low-cost input-output devices to display the information, and compact optical systems architectures and designs. We propose to use a nonlinear filter based optoelectronics neural networks associated with a supervised perception learning algorithm for real-time face recognition. The first layer is implemented optically using a nonlinear joint transform correlator (JTC) 2-4 and the second layer is implemented electronically because of the small number of the hidden layer neurons. The system is trained with a sequence of input facial images and is able to classify an input face in real-time. Based on the characteristics of the nonlinear JTC, the proposed system has the following features: it is easy to implement optically and is robust in terms of system alignment; the system can be integrated into a low-cost compact prototype; the system is trained by updating the reference images (weights)in the input which can be stored in electronic or optical memories and no filters or holograms need to be produced; using nonlinear transformation in the Fourier plane, the system is robust to ill umination variations of the input image, has a good discrimination sensitivity and is robust to noise; and the system is shift invariant. The processor may use commercially available opto-electronics devices and can be built as a low cost compact system. Computer simulations and optical experimental results will be performed to determine the probability of error of the system in identifying input facial images. By using time multiplexing of the input image under investigation, we hope that using more than one input image, the probability of error for classification can be reduced to zero. ***
