Additional Info

• First Name: Patrick
• Last Name: Grother
• Priority: Normal
• Job Title: Scientist
• Company: National Institute of Standards in Technology (NIST)
• Country: United States of America
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• Session Timeslot: Thursday 15/10/2026 16:50-17:10
• Session Title: Session 8 __ Phygital identity: navigating between the physical and digital worlds
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• Session Track: Identity
• Session Location: Audi 15
• Role: Speaker
• Bio:

  Patrick Grother is a scientist at the National Institute of Standards in Technology (NIST) responsible for biometric algorithm evaluation, and biometric performance testing standardization. He leads the Face Recognition and Analysis Technology Evaluations (FRTE and FATE, formerly FRVT) which constitute the world’s largest independent public tests of face recognition and face analysis algorithms. These give quantitative support to developers, end-users and policy makers faced with algorithm selection, performance adequacy assessment, and procurement specification. His current research interests relate to biometric failure analysis, image quality, demographic effects, and scalability.

  Patrick co-chairs NIST’s biannual International Face Performance Conference (IFPC) on measurement, metrics, and certification. He was a committee member and author of the National Academies’ 2024 report Facial Recognition Technology: Current Capabilities, Future Prospects, and Governance. He assists several US Government agencies in biometrics performance assessment and standardization. Since 2018, he has served as the chairman of the ISO/IEC/JTC 1 Subcommittee 37 on Biometrics where he has edited multiple performance testing and data interchange standards. He received the IEC 1906 Award in 2009, the ANSI Lohse IT Medal in 2013, and U.S. Department of Commerce Gold Medals in 2007, 2012 and 2021.

• Presentation Title: Toward operational morph detection
• Presentation 2-Line Description: With advances in automated morph detection, the talk will describe options for their deployment and procedures to be followed should a detection occur. The talk will include state of the art in MAD, and results on operational datasets.
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• Profile: Identity
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