Sep
24
Date: 24-September-2026
Time: 9:00 AM ET (New York Time)
Presenter: Dr. Vishal Patel
Abstract
What if the next generation of biometrics is not built on pixels at all? Biometrics has traditionally relied on discrete pixel representations and, more recently, learned feature embeddings. This webinar explores an emerging alternative: Implicit Neural Representations (INRs), which model biometric traits as continuous functions rather than discrete images.
The talk will introduce the principles behind INRs and show how they can be applied to deepfake and presentation attack detection, biometric quality assessment, and function-space metric learning. It will also look ahead to their potential role in biometric recognition, continuous biometric templates, morph attack analysis, privacy-preserving biometrics, and multimodal biometric systems. Could continuous neural representations provide a new foundation for the next generation of biometric technologies? Join us to find out.
Biography
Dr. Vishal Patel
Vishal Patel is a Professor in the Department of Electrical and Computer Engineering (ECE) at Johns Hopkins University. His research focuses on computer vision, machine learning, image processing, medical image analysis, and biometrics. He has received a number of awards including the 2021 IEEE Signal Processing Society (SPS) Pierre-Simon Laplace Early Career Technical Achievement Award, the 2021 NSF CAREER Award, the 2021 IAPR Young Biometrics Investigator Award (YBIA), the 2016 ONR Young Investigator Award, and the 2016 Jimmy Lin Award for Invention. Patel serves as an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence journal and IEEE Transactions on Biometrics, Behavior, and Identity Science. He is a Fellow of the IEEE and IAPR.
