Glaser, Christian (2013)
Face Liveness Detection Against Image and Video Spoofing Attacks.
Technische Universität Darmstadt
Masterarbeit, Bibliographie
Kurzbeschreibung (Abstract)
Many situations require users to log into a computer system. For example to perform private tasks like banking, social interaction or to get access to a secured area. Conventional security driven systems have the disadvantage that passwords or keycards are needed. These passwords or keycards can get lost or stolen resulting in a security risk. To overcome this drawback biometrics use the characteristics of the human body to grand access to a computer system. Beside fingerprint or iris recognition face detection is a popular biometric trait. The reason therefore is that it requires only a usual camera. Most of the current systems have a camera build in anyway. Also face recognition is not very intrusive to the user, which gives a high acceptability of face recognition is biometric trait. In the past though face recognition systems could easily be tricked due to spoofing attempts using pictures or videos of the authenticate user. This thesis analyses current algorithms to counter such spoofing attempts and presents a novel approach. The presented approach will use Machine Learning and Computer Vision to utilize an algorithms by Wu et al. WRS_12 that can magnify subtle changes in videos to reveal the human pulse. An evaluation of the feasibility of this approach will be given.
| Typ des Eintrags: | Masterarbeit |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Glaser, Christian |
| Art des Eintrags: | Bibliographie |
| Titel: | Face Liveness Detection Against Image and Video Spoofing Attacks |
| Sprache: | Englisch |
| Publikationsjahr: | 2013 |
| Kurzbeschreibung (Abstract): | Many situations require users to log into a computer system. For example to perform private tasks like banking, social interaction or to get access to a secured area. Conventional security driven systems have the disadvantage that passwords or keycards are needed. These passwords or keycards can get lost or stolen resulting in a security risk. To overcome this drawback biometrics use the characteristics of the human body to grand access to a computer system. Beside fingerprint or iris recognition face detection is a popular biometric trait. The reason therefore is that it requires only a usual camera. Most of the current systems have a camera build in anyway. Also face recognition is not very intrusive to the user, which gives a high acceptability of face recognition is biometric trait. In the past though face recognition systems could easily be tricked due to spoofing attempts using pictures or videos of the authenticate user. This thesis analyses current algorithms to counter such spoofing attempts and presents a novel approach. The presented approach will use Machine Learning and Computer Vision to utilize an algorithms by Wu et al. WRS_12 that can magnify subtle changes in videos to reveal the human pulse. An evaluation of the feasibility of this approach will be given. |
| Freie Schlagworte: | Business Field: Digital society, Research Area: Confluence of graphics and vision, Liveness detection, Biometrics, Security technologies, Computer vision, Spoofing attacks |
| Zusätzliche Informationen: | 92 p. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum