A Bibliometric Analysis of Face Presentation Attacks based on Domain Adaptation

Authors

Smita C. Khairnar, Symbiosis International University and Pimpri Chinchwad College of Engineering, SPPU, Pune, IndiaFollow
Shilpa Shailesh Gite Dr., Symbiosis Institute of Technology (SIT), Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International University, Lavale, Pune, IndiaFollow
Sudeep D. Thepade Dr., Pimpri Chinchwad College of Engineering, SPPU, Pune, IndiaFollow

Document Type

Article

Abstract

Background: This research aims to look into the work that has been done on Face Presentation attacks and detection with domain adaptation techniques from 2011 to 2021 utilizing bibliography methods.

Approaches: Diverse research articles on Face Presentation Attacks were retrieved using the two most popular databases Web of Science & Scopus. Research articles consider between 2011-2021. Some research results, such as documents by year, documents by source, documents by funding agencies, countries, etc are obtained using Web of Science and Scopus Analyzers. The analysis is performed using Vos Viewer version 1.6.16 and various parameters such as keywords, co-authorship, co-occurrences, citation analysis, and so on.

Results: We present findings for both the Web of Science and Scopus datasets in this paper. As a result of primary keywords as Face Presentation attacks and secondary keywords as domain adaptation-based Face anti-spoofing, the total no of documents is 117 and 151 respectively retrieved. The maximum number of documents are published in the year 2020, most of the research is carried out by China as it has maximum funding research agencies.

Conclusions: The purpose of using two databases for analysis in this study is to reduce the efforts of research scholars in analyzing the two most popular databases separately. Research documents are analyzed based on various parameters indicates that the research topic has a very good potential. The network study of various parameters shows that there is a lot of space for contribution in terms of domain adaptation, generalization, adversarial attacks, GAN, machine learning, and deep learning in future research.