Date: 28 November 2023
Time: 9:00 AM ET (New York Time)
Presenter(s): Dr. Juan Tapia
Original Article: Open Access - Freely available for download.
Published: IEEE Transactions on Information Forensics and Security, December 2021
Iris pattern recognition has significantly improved biometric authentication due to its high stability and uniqueness. Such physical characteristics have been essential in security applications and other related areas. However, presentation attacks, also known as spoofing techniques, can bypass biometric authentication systems using artefacts such as printed images, artificial eyes, textured contact lenses, and others. Today, many liveness detection methods that improve the robustness of these systems have been proposed. An ideal Presentation Attack Detection (PAD) technique should be able to detect all these attacks, along with any new or unknown presentation attack instrument species that may be developed in the future. PAD for iris recognition systems is a very dynamic topic, as shown in past editions of the LivDet competition, revealing that there are still open problems to get efficient methods for capturing devices. This work contributes to improving the state of the art, adds a new database and also explains the methodology used for our team.
Dr. Juan Tapia received the P.E. degree in Electronics Engineering from Universidad Mayor in 2004, the M.S. degree in Electrical Engineering and the Ph.D. degree from the Department of Electrical Engineering, from the Universidad de Chile in 2012 and 2016, respectively. In addition, he spent one year of internship at the University of Notre Dame. In 2016, he received the award for best PhD thesis.
He is currently an Entrepreneur and Senior Researcher at Hochschule Darmstadt (HDA). From 2016 to 2017 was an Assistant Professor at Universidad Andres Bello. From 2018 to 2020, he was the R&D Director for the Electricity and Electronics area at Universidad Tecnologica de Chile – INACAP, R&D Director of TOC Biometrics company, consultor on biometrics iris applications and Forensic ID-cards.
Dr. Tapia’s main research interests include pattern recognition and deep learning applied to iris biometrics, morphing, feature fusion, and feature selection.