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Real Time Face Detection and Recognition using Rectangular Feature based Classifier and Class Matching Algorithm

사각형 특징 기반 분류기와 클래스 매칭을 이용한 실시간 얼굴 검출 및 인식

  • 김종민 (조선대학교 대학원 전산통계학과) ;
  • 강명아 (광주대학교 컴퓨터공학과)
  • Published : 2010.01.28

Abstract

This paper proposes a classifier based on rectangular feature to detect face in real time. The goal is to realize a strong detection algorithm which satisfies both efficiency in calculation and detection performance. The proposed algorithm consists of the following three stages: Feature creation, classifier study and real time facial domain detection. Feature creation organizes a feature set with the proposed five rectangular features and calculates the feature values efficiently by using SAT (Summed-Area Tables). Classifier learning creates classifiers hierarchically by using the AdaBoost algorithm. In addition, it gets excellent detection performance by applying important face patterns repeatedly at the next level. Real time facial domain detection finds facial domains rapidly and efficiently through the classifier based on the rectangular feature that was created. Also, the recognition rate was improved by using the domain which detected a face domain as the input image and by using PCA and KNN algorithms and a Class to Class rather than the existing Point to Point technique.

Keywords

SAT(Summed-Area Tables);Principal Component Analysis;K-Nearest-Neighbor

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