Face detection is a widely studied topic in computer vision, and advances in algorithms, low cost processing, and CMOS imagers make it practical for embedded consumer applications. As with graphics, the best cost-performance ratio is achieved with dedicated hardware. The challenges of face detection in embedded environments include bandwidth constraints set by low cost memory and a need to find parallelism. Consumer applications need reliability, calling for a hard real-time approach to guarantee that deadlines are met. We present a face detection system for automatic exposure control in a handheld digital camera or camera phone. Contributions include a complexity control scheme to meet hard real-time deadlines, a hardware pipeline design for Haar-like feature calculation, and a system design exploiting several levels of parallelism. The proposed architecture is verified by synthesis to Altera's low cost Cyclone II FPGA. Simulation results show the algorithm can achieve about 80% detection rate for group portrait pictures.