English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Gait patterns have been used widely in recent years to authenticate users. Because it doesn’t require user intrusion, it is often used as a biometric to make authentication processes easier and hassle free. But there are various issues with this process. To start with, the viewing angle has to be constant which is quite difficult to achieve with limited number of cameras. Speed too can alter the way a person walks and cause inconsistencies in identification. Furthermore, complications might arise if the subject is carrying something. The weight can affect his walking pattern. Besides, a recent accident could also transform a person’s walking pattern and lead to wrong identification. Other biometrics such as face detection can be combined with this technique to reduce the issues leading to erroneous identification. In this paper, we propose a system to overcome the viewing angle discrepancies. The system takes in walking sequences as input and processes them to create images. This is converted into 3D images by means of stereovision algorithms. Using which, we can effectively match the real-time image with various image sequences in the database. Side face detection can enhance the accuracy further..

Gait Recognition as Non-Intrusive Biometric Using View Invariant Methods in Multi Temporal Images