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There is a rapidly growing demand for using smart cameras for various biometric applications in surveillance. Although having a small form-factor, most of these applications demand huge processing performance for real-time processing. Face recognition is one of those applications. In this paper we show that we can run face recognition in real-time by implementing the algorithm on an architecture which combines a parallel pixel processor: with a digital signal processor. The algorithm consists of a cascade of filters for detection, registration and normalization and an RBF neural network with temporal filtering. Everything fits within a digital camera, the size of a normal surveillance camera.