|Title:||A Study on the Application of Walking Posture for Identifying Persons with Gait Recognition||Authors:||Tsai, Yu-Shiuan
|Keywords:||deep learning;human skeleton;identity recognition;walking posture;behavior recognition;LSTM;single camera;OpenPose||Issue Date:||1-Aug-2022||Publisher:||MDPI||Journal Volume:||12||Journal Issue:||15||Source:||APPLIED SCIENCES-BASEL||Abstract:||
In terms of gait recognition, face recognition is currently the most commonly used technology with high accuracy. However, in an image, there is not necessarily a face. Therefore, face recognition cannot be used if there is no face at all. However, when we cannot obtain facial information, we still want to know the person's identity. Thus, we must use information other than facial features to identify the person. Since each person's behavior will be somewhat different, we hope to learn the difference between one specific human body and others and use this behavior to identify the human body because deep learning technology advances this idea. Therefore, we used OpenPose along with LSTM for personal identification. We found that using people's walking posture is feasible for identifying their identities. Presently, the environment for making judgments is limited, in terms of height, and there will be restrictions on distance. In the future, using various angles and distances will be explored. This method can also solve the problem of half-body identification and is also helpful for finding people.
|Appears in Collections:||資訊工程學系|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.