Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Gait-based Human Identification System using Eigenfeature Regularization and Extraction

고유특징 정규화 및 추출 기법을 이용한 걸음걸이 바이오 정보 기반 사용자 인식 시스템

  • 이병윤 (연세대학교 전기전자공학부) ;
  • 홍성준 (연세대학교 전기전자공학부) ;
  • 이희성 (연세대학교 전기전자공학부) ;
  • 김은태 (연세대학교 전기전자공학부)
  • Received : 2010.10.21
  • Accepted : 2010.12.10
  • Published : 2011.02.25
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Abstract

In this paper, we propose a gait-based human identification system using eigenfeature regularization and extraction (ERE). First, a gait feature for human identification which is called gait energy image (GEI) is generated from walking sequences acquired from a camera sensor. In training phase, regularized transformation matrix is obtained by applying ERE to the gallery GEI dataset, and the gallery GEI dataset is projected onto the eigenspace to obtain galley features. In testing phase, the probe GEI dataset is projected onto the eigenspace created in training phase and determine the identity by using a nearest neighbor classifier. Experiments are carried out on the CASIA gait dataset A to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than previous works in terms of correct classification rate.

본 논문에서는 고유특징 정규화 및 추출 기법(ERE: Eigenfeature Regularization and Extraction)을 이용한 걸음걸이 바이오 정보 기반 사용자 인식 시스템을 제안한다. 먼저 카메라 센서에서 취득한 걸음걸이 시퀀스로부터 사용자 인식을 위한 특징 정보로 걸음걸이 에너지 영상(GEI: Gait Energy Image)을 생성한다. 학습 단계에서는 갤러리 걸음걸이 에너지 영상에 ERE를 적용하여 정규화된 변환행렬을 획득하여 고유공간(eigenspace)에 사상된 특징정보를 구하고, 검증 단계에서는 걸음걸이 에너지 영상을 학습단계에서 생성한 고유공간에 사상하여 최근접 이웃 분류기를 이용하여 사용자를 인식한다. 제안한 시스템의 유효성 검증을 위해 CASIA 걸음걸이 데이터셋 A를 이용하여 실험하였고, 기존 연구에 비해 인식 정확도 면에서 우수한 성능을 보여주었다.

Keywords

References

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