身体部位の特徴点と形状情報に基づくモデルベース歩容認証の検討  [in Japanese] Study on Model-based Gait Recognition Based on Body Points and Local Shape Information  [in Japanese]

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Author(s)

Abstract

Gait Recognition is recently evolving techniques by which we can recognize individuals by one's gait. There are two major approaches; silhouette-based and model-based. In Japan, a method based on GEI (Gait Energy Image), which is one of the silhouette-based approaches, is beginning to be used for forensic purposes. It is a problem, however, that person's silhouettes' variabilities due to conditions such as view angles, resolutions, frame-rates, and body regions used for analysis, and so on, sometimes lessen recognition reliability under the use of GEI method only. It is an urgent task to expand the ranges of application. In order to try to resolve the task, it could be valid to use several different approaches, including model-based ones. Previous studies of model-based methods can be divided in two types; manual and automated. However, it is a problem that manual methods require a person of special skills and experiences, and automated methods only can apply specific view condition, so it is required to develop finer model-based methods. Here, we proposed a novel model-based-like method based on feature points (resemble body joints) and local shape features around feature points. We examined error rates of the proposed method under various conditions of frame-rates, resolutions, and regions used in analysis, and compared them with those of GEI method. We found that the proposed method showed a little higher recognition rate than GEI one when captured from lateral direction and when upper body is used in analysis, and showed less dependency on low frame-rate conditions than GEI one when captured from lateral direction.

Journal

  • Journal of the Japan Society for Precision Engineering

    Journal of the Japan Society for Precision Engineering 83(1), 94-100, 2017

    The Japan Society for Precision Engineering

Codes

  • NII Article ID (NAID)
    130005180690
  • Text Lang
    JPN
  • ISSN
    0912-0289
  • Data Source
    J-STAGE 
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