Cyber-Forensic Review of Human Footprint and Gait for Personal Identification

[2204.09344] Cyber-Forensic Review of Human Footprint and Gait for Personal Identification Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2204.09344 (cs) [Submitted on 20 Apr 2022] Title: Cyber-Forensic Review of Human Footprint and Gait for Personal Identification Authors: Kapil Kumar Nagwanshi View PDF HTML (experimental) Abstract: The human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore, it is very crucial to recovering the footprints from identifying the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time the world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defence, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints quickly. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) MSC classes: 68U10 ACM classes: I.4 Report number: https://www.iaeng.org/IJCS/issues_v46/issue_4/IJCS_46_4_15.pdf Cite as: arXiv:2204.09344 [cs.CV] (or arXiv:2204.09344v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2204.09344 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IAENG International Journal of Computer Science, vol. 46, no.4, pp645-661, 2019 Submission history From: Kapil Nagwanshi [ view email ] [v1] Wed, 20 Apr 2022 09:29:43 UTC (5,978 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2022-04 Change to browse by: cs cs.AI cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Cyber-Forensic Review of Human Footprint and Gait for Personal Identification#682363
• Lie Detector At the Gait: Artificial Intelligence Model#1152348
• Behavioural Biometrics for Continuous Authentication in Cybersecurity Systems#100240
• New Area Based Measures for Gait Recognition#824268
• New Area Measures for Automatic Gait Recognition#824267