TAILIEUCHUNG - EURASIP Journal on Applied Signal Processing 2003:9, 878–889 c 2003 Hindawi Publishing

EURASIP Journal on Applied Signal Processing 2003:9, 878–889 c 2003 Hindawi Publishing Corporation Face Detection Using a First-Order RCE Classifier Byeong Hwan Jeon Signal Processing Laboratory, School of Electrical Engineering, Seoul National University, Seoul 151-742, Korea Institute of Intelligent Systems, Mechatronics Center, Samsung Electronics Co., Ltd. Suwon, Gyeonggi-Do 442-742, Korea Email: jeon@ Kyoung Mu Lee Department of Electronics and Electrical Engineering, Hong-Ik University, Seoul 121-711, Korea Email: kmlee@ Sang Uk Lee Signal Processing Laboratory, School of Electrical Engineering, Seoul National University, Seoul 151-742, Korea Email: sanguk@ Received 9 September 2002 and in revised form 9 April 2003 We present a new face detection algorithm based on a. | EURASIP Journal on Applied Signal Processing 2003 9 878-889 2003 Hindawi Publishing Corporation Face Detection Using a First-Order RCE Classifier Byeong HwanJeon Signal Processing Laboratory School of Electrical Engineering Seoul National University Seoul 151-742 Korea Institute of Intelligent Systems Mechatronics Center Samsung Electronics Co. Ltd. Suwon Gyeonggi-Do 442-742 Korea Email jeon@ Kyoung Mu Lee Department of Electronics and Electrical Engineering Hong-Ik University Seoul 121-711 Korea Email kmlee@ Sang Uk Lee Signal Processing Laboratory School of Electrical Engineering Seoul National University Seoul 151-742 Korea Email sanguk@ Received 9 September 2002 and in revised form 9 April 2003 We present a new face detection algorithm based on a first-order reduced Coulomb energy RCE classifier. The algorithm locates frontal views of human faces at any degree of rotation and scale in complex scenes. The face candidates and their orientations are first determined by computing the Hausdorff distance between simple face abstraction models and binary test windows in an image pyramid. Then after normalizing the energy each face candidate is verified by two subsequent classifiers a binary image classifier and the first-order RCE classifier. While the binary image classifier is employed as a preclassifier to discard nonfaces with minimum computational complexity the first-order RCE classifier is used as the main face classifier for final verification. An optimal training method to construct the representative face model database is also presented. Experimental results show that the proposed algorithm yields a high detection ratio while yielding no false alarm. Keywords and phrases face detection face model Hausdorff distance clustering algorithm RCE classifier. 1. INTRODUCTION In recent years due to the potential applications in many fields including surveillance authentication video indexing and so forth face detection and

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