Face Recognition Based on PCA and LDA Combining Clustering

Clustering을 결합한 PCA와 LDA 기반 얼굴 인식

  • Guo, Lian-Hua (School of Electrical Engineering and Computer Science, Seoul National University) ;
  • Kim, Pyo-Jae (School of Electrical Engineering and Computer Science, Seoul National University) ;
  • Chang, Hyung-Jin (School of Electrical Engineering and Computer Science, Seoul National University) ;
  • Choi, Jin-Young (School of Electrical Engineering and Computer Science, Seoul National University)
  • 곽련화 (서울대학교 공과대학 전기.컴퓨터 공학부) ;
  • 김표재 (서울대학교 공과대학 전기.컴퓨터 공학부) ;
  • 장형진 (서울대학교 공과대학 전기.컴퓨터 공학부) ;
  • 최진영 (서울대학교 공과대학 전기.컴퓨터 공학부)
  • Published : 2006.06.21

Abstract

In this paper, we propose an efficient algorithm based on PCA and LDA combining K-means clustering method, which has better accuracy of face recognition than Eigenface and Fisherface. In this algorithm, PCA is firstly used to reduce the dimensionality of original face image. Secondly, a truncated face image data are sub-clustered by K-means clustering method based on Euclidean distances, and all small subclusters are labeled in sequence. Then LDA method project data into low dimension feature space and group data easier to classify. Finally we use nearest neighborhood method to determine the label of test data. To show the recognition accuracy of the proposed algorithm, we performed several simulations using the Yale and ORL (Olivetti Research Laboratory) database. Simulation results show that proposed method achieves better performance in recognition accuracy.

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