Department of Biocybernetics and Biomedical Engineering of the Faculty of Mechanical Engineering at Bialystok University of Technology, 15-351 Bialystok, Poland.
Department of Automatic Control and Robotics of the Faculty of Mechanical Engineering at Bialystok University of Technology, 15-351 Bialystok, Poland.
Sensors (Basel). 2018 May 21;18(5):1639. doi: 10.3390/s18051639.
Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR’s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method.
生物识别技术目前是一个非常有趣且快速发展的领域。在各种类型的生物识别技术中，人类步态识别似乎是最引人注目的技术之一。然而，生物识别技术领域的最大问题之一是由于鞋类而导致的步态变化。换鞋会导致识别人员的准确性显著降低。以下工作提出了一种使用两个传感器（力板和 Microsoft Kinect v2）收集的数据来解决此问题的方法。Microsoft Kinect 用于测量人体的身高，这允许将识别的人员范围缩小到那些身高与已测量身高相似的人员。整个过程首先要识别人员所穿的鞋类类型。该研究是在从 99 个人（超过 3400 步）获得的数据上进行的，所提出的方法使我们能够达到超过 88％的正确分类率（CCR），与早期达到 CCR 小于 80％的方法相比，这是一个重大改进。该工作介绍了所提出方法的优点和局限性。
