Face detection is a widely studied topic in computer vision, and recent 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. In this paper, we design an embedded face detection system for handheld digital cameras or camera phones. The challenges of face detection in embedded environments include an efficient pipeline design, bandwidth constraints set by low cost memory, a need to find parallelism, and how to utilize the available hardware resources efficiently. In addition, consumer applications require reliability which calls for a hard real-time approach to guarantee that processing deadlines are met. Specifically, the main contributions of the paper include: (1) incorporation of a Genetic Algorithm in the AdaBoost training to optimize the detection performance given the number of Haar features; (2) a complexity control scheme to meet hard real-time deadlines; (3) 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 system can achieve about 75-80% detection rate for group portraits.
['Ming Yang', 'James E. Crenshaw', 'Bruce Augustine', 'Russell Mareachen', 'Ying Wu']
AdaBoost-based face detection for embedded systems