COMMUNICATION-AWARE FACE DETECTION USING NOC ARCHITECTURE Hung-Chih Lai, Radu Marculescu, Marios Savvides, and Tsuhan Chen Department of Electrical and Computer Engineering Carnegie Mellon University, Pittsburgh, PA 15213, USA {hlai, radum, marioss, tsuhan}@cmu.edu Abstract. Face detection is an essential first step towards many advanced computer vision, biometrics recognition and multimedia applications, such as face tracking, face recognition, and video surveillance. In this paper, we proposed an FPGA hardware design with NoC (Network-on-Chip) architecture based on an AdaBoost face detection algorithm. The AdaBoost-based method is the state-of-the-art face detection algorithm in terms of speed and detection rates and the NoC provides high communication capability architecture. This design is verified on a Xilinx Virtex-II Pro FPGA platform. Simulation results show the improvement in speed 40 frames per second compared to software implementation. The NoC architecture provides scalability so that our proposed face detection method can be sped up by adding multiple classifier modules. Keywords: Face detection, Hardware Architecture, Network-on-Chip. 1 Introduction Face detection is the process of finding all possible faces in a given image or a video sequence. More precisely, face detection has to determine the locations and sizes of all possible human faces. It is a more complex case than face localization in which the number of faces is already known. On the other hand, face detection is the essential first step towards many advanced computer vision, biometrics recognition and multimedia applications, such as face tracking, face recognition, and video surveillance. Due to scale, rotation, pose and illumination variation, face detection involves many research challenges. How to detect different scales of faces, how to be robust to illumination variation, how to achieve high detection rate with low false detection rates are only few of all issues a face detection algorithm needs to consider. Face detection techniques have been researched for years and much progress has been proposed in literature. Most of the face detection methods focus on detecting frontal faces with good lighting conditions. According to Yang’s survey [6], these methods can be categorized into four types: knowledge-based, feature invariant, template matching and appearance-based. Knowledge-based methods use human-coded rules to model facial features, such as two symmetric eyes, a nose in the middle and a mouth underneath the nose [12].