FPGA Based Face Detection System Using Haar Classifiers
This paper presents a hardware architecture for face detection based system on AdaBoost algorithm using Haar features. We describe the hardware design techniques including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the processing speed of the face detection system. Also we discuss the optimization of the proposed architecture which can be scalable for configurable devices with variable resources. The proposed architecture for face detection has been designed using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured and compared with an equivalent software implementation. We show about 35 times increase of system performance over the equivalent software implementation. Categories and Subject Descriptors
Face detection in image sequence has been an active research area in the computer vision field in recent years due to its potential applications such as monitoring and surveillance [1], human computer interfaces [2], smart rooms [3], intelligent robots [4], and biomedical image analysis [5]. Face detection is based on identifying and locating a human face in images regardless of size, position, and condition. Numerous approaches have been proposed for face detection in images. Simple features such as color, motion, and texture are used for the face detection in early researches. However, these methods break down easily because of the complexity of the real world. Face detection proposed by Viola and Jones [6] is most popular among the face detection approaches based on statistic methods. This face detection is a variant of the AdaBoost algorithm [7] which achieves rapid and robust face detection. They proposed a face detection framework based on the AdaBoost learning algorithm using Haar features. However, the face detection requires considerable computation power because many Haar feature classifiers check all pixels in the images. Although real-time face detection is possible using high performance computers, the resources of the system tend to be monopolized by face detection. Therefore, this constitutes a bottleneck to the application of face detection in real time.
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