Dynamic time warping based identification using Gabor feature of Adaptive Motion Model for walking humans

Junbum Park, Younghyun Lee, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this paper, we propose a novel feature extraction method for the identification of humans. The main objective of our method is to identify each human being by extracting the Gabor feature based on the Adaptive Motion Model (AMM) for the motion of humans. In our method, the adaptive motion model, which can represent the temporal motion for each walking human is first made from the sequence images and, then, the Gabor features of the eight directions which can represent the spatial motion information for humans are extracted. The proposed feature extraction method can make a more accurate motion model by adjusting the weight between the previous and current model for each person. Moreover, our method has the advantage of allowing more information such as the Gabor features for the eight directions extracted from the AMM. Since the conventional method uses the face feature for each human being, it has disadvantages in the case of images of small size, while our method has better identification performance this case, because it only uses the spatio-temporal motion information. Finally, we identify each person by finding the minimum value of the extended dynamic time warping (DTW) for the eight Gabor features. The accuracy of the identification conducted using the proposed feature is better than that of the conventional method using the Gait Energy Image (GEI) and Face Image feature.

Original languageEnglish
Pages (from-to)817-823
Number of pages7
JournalInternational Journal of Control, Automation and Systems
Issue number5
Publication statusPublished - 2009


  • Adaptive Motion Model (AMM)
  • Dynamic time warping (DTW)
  • Gabor feature
  • Gait Energy Image
  • Human identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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