Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Infrared Gait Recognition using Wavelet Transform and Linear Discriminant Analysis

웨이블릿 변환과 선형 판별 분석법을 이용한 적외선 걸음걸이 인식

  • Kim, SaMun (Department of Control and Robotics Engineering, Chungbuk University) ;
  • Lee, DaeJong (Department of Control and Robotics Engineering, Chungbuk University) ;
  • Chun, MyungGeun (Department of Control and Robotics Engineering, Chungbuk University)
  • 김사문 (충북대학교 제어로봇공학과, 컴퓨터정보통신연구소) ;
  • 이대종 (충북대학교 제어로봇공학과, 컴퓨터정보통신연구소) ;
  • 전명근 (충북대학교 제어로봇공학과, 컴퓨터정보통신연구소)
  • Received : 2014.09.14
  • Accepted : 2014.12.08
  • Published : 2014.12.25
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Abstract

This paper proposes a new method which improves recognition rate on the gait recognition system using wavelet transform, linear discriminant analysis and genetic algorithm. We use wavelet transform to obtain the four sub-bands from the gait energy image. In order to extract feature data from sub-bands, we use linear discriminant analysis. Distance values between training data and four sub-band data are calculated and four weights which are calculated by genetic algorithm is assigned at each sub-band distance. Based on a new fusion distance value, we conducted recognition experiments using k-nearest neighbors algorithm. Experimental results show that the proposed weight fusion method has higher recognition rate than conventional method.

본 논문은 웨이블릿 변환과 선형 판별 분석법 그리고 유전알고리즘을 이용하여 걸음걸이 인식률을 향상시키는 방법을 제안한다. 걸음걸이 에너지 영상에서 웨이블릿 변환으로 분해된 4개의 대역을 얻는다. 분해된 대역을 선형 판별 분석법으로 영상의 특징을 추출한다. 추출된 4개 대역의 특징들과 학습영상의 특징들 사이의 유클리디안 거리를 계산하고, 각 대역에서 계산된 거리 값에 유전알고리즘으로 최적화된 4개의 가중치를 부여한다. 4개 대역의 거리 값과 가중치와의 선형결합으로 계산된 새로운 거리 값을 바탕으로 최근접 이웃 분류 방법을 이용하여 인식 실험을 수행한다. 실험 결과에서 가중치 융합 전 인식률 보다 융합 후 인식률이 더 높은 것을 확인 할 수 있다.

Keywords

References

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