We propose a near real-time face recognition system for embedding in consumer applications. The system is embedded in a networked home environment and enables personalized services by automatic identification of users. The aim of our research is to design and build a face recognition system that is robust for natural consumer environments and can be executed on low-cost hardware. For enabling distribution of computations, we propose a processing pipeline for face recognition, which consists of (I) face detection by stepwise pruning; (2) coarse-to-fine facial feature extraction for face normalization; and (3) face identification by cascaded discriminant analysis. The system has been applied in varying environments, such as an experimental home network, and achieves over 95% recognition rate and 3-4 frames/s processing speed.