Face Detection Using a First-Order RCE Classifier
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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: [email protected]
Kyoung Mu Lee Department of Electronics and Electrical Engineering, Hong-Ik University, Seoul 121-711, Korea Email: [email protected]
Sang Uk Lee Signal Processing Laboratory, School of Electrical Engineering, Seoul National University, Seoul 151-742, Korea Email: [email protected] 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.
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INTRODUCTION
In recent years, due to the potential applications in many fields, including surveillance, authentication, video indexing, and so forth, face detection and recognition problems have gained much attention in computer vision society. The face detection problem is to locate human faces in a scene or a sequence of images, and the face detection technique not only can be used as a key preprocessing step for face recognition but also has its own importance in several applications, such as tracking, video indexing, and so on. In general, the face detection problem is known to be very difficult due to the variations in race, gender, pose, expressions, adornments, illumination, and scale. Face detection can be considered as a pattern recognition problem and can be solved by statistical pattern classification techniques [1, 2], yielding the Boolean output: face or nonface. Functionally, well-organized parametric classifiers, such as Bayesian classifier [3], artificial neural network [4, 5], support vector machine [6, 7], have been used to classify the feature vectors by supervised classification techniques in the
feature space. These parametric classifiers for face detection use high degree of data abstraction such as a set of trained weights, coefficients, or probabilities. U
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