Please use this identifier to cite or link to this item: http://dspace.dtu.ac.in:8080/jspui/handle/repository/20186
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKUMAR, MANISH-
dc.date.accessioned2023-08-18T06:34:40Z-
dc.date.available2023-08-18T06:34:40Z-
dc.date.issued2023-05-
dc.identifier.urihttp://dspace.dtu.ac.in:8080/jspui/handle/repository/20186-
dc.description.abstractWith the advancements in technology, face recognition systems have become increasingly prevalent in various applications, ranging from security systems to user authentication. However, these systems are susceptible to spoofing attacks, where adversaries attempt to deceive the system by presenting manipulated or fake face images. To address this vulnerability, numerous face anti-spoofing detection approaches have been proposed. And with the rise in sophisticated spoofing attacks, it is crucial to evaluate and compare different face anti-spoofing detection approaches to identify their strengths, weaknesses, and overall performance. This thesis presents a comprehensive comparative analysis of various face anti-spoofing detection approaches, including traditional methods and deep learning-based techniques. The objective is to assess their effectiveness in detecting and differentiating genuine faces from spoofed faces, considering different types of spoofing attacks and datasets. The analysis includes evaluation metrics such as accuracy, false acceptance rate, false rejection rate, and receiver operating characteristic curves. The findings of this study provide valuable insights into the strengths and limitations of different approaches, enabling researchers and practitioners to make informed decisions when choosing face anti-spoofing techniques for real-world applicationsen_US
dc.language.isoenen_US
dc.relation.ispartofseriesTD-6726;-
dc.subjectFACE RECOGNITION SYSTEMen_US
dc.subjectFACE ANTI SPOOFING DETECTIONen_US
dc.subjectSPOOFING ATTACKSen_US
dc.subjectFAKE FACE IMAGESen_US
dc.titleA COMPARATIVE ANALYSIS ON FACE ANTI SPOOFING DETECTION APPROACHESen_US
dc.typeThesisen_US
Appears in Collections:M.E./M.Tech. Computer Engineering

Files in This Item:
File Description SizeFormat 
MANISH KUMAR M.Tech..pdf686.95 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.