Title of the Talk: Biometric Sample Quality: the Recognition Model Perspective
Speaker: Naser Damer, Fraunhofer Institute for Computer Graphics Research IGD, Germany
Abstract: Biometric sample quality measures the utility of the sample to the recognition algorithm. The relevance of the sample quality to different processes in biometric systems attracted the attention of different stakeholders. This interest was clear in the recent NIST evaluation “Face Analysis Technology Evaluation (FATE) Quality” and the ever-evolving ISO standardization effort, including the ISO/IEC 29794-1. Given face recognition as an example, this talk will first present an overview of the main aspect of modern face recognition models, their training, and the template inference process. Based on that, the talk will discuss different aspect of the recognition model behavior that can present indications related to sample quality. Such behavior observations are perceived both in the inference and training process of modern face recognition models and, if probably understood, can be translated into quality scores inherently indicating the utility of biometric samples.
Title of the Talk: Human Recognition at a Distance in Video
Speaker: Bir Bhanu, University of California, Riverside
Abstract: Most biometric systems employed for human recognition require physical contact with, or close proximity to a cooperative subject. Far more challenging is the ability to reliably recognize non-cooperative individuals at a distance when viewed from an arbitrary angle under real-world changing environments. Gait, face and body are the biometrics that can be most easily captured from a distance using video cameras mounted on a land or aerial platforms. However, there are many technical challenges in the real-world that include imaging distortions, imaging range, lack of enough video frames, arbitrary pose, occlusions, air turbulence, and changing clothes. This talk will present both 2D and 3D representations and methods and their evaluation for robust video-based human recognition at a distance in the wild.
Title of the Talk: Recent Advances in Privacy-Preserving Biometrics Authentication
Speaker: Jiankun Hu, University of New South Wales, Australia
Abstract: It is well-known that biometrics can provide an authentication of genuine users. Significant advances have been made in the field with many successful applications, e.g., border control and digital access control. Biometrics involves a person’s privacy data which is regulated by laws in many countries. There is a trend/need to develop privacy-preserving biometrics authentication technologies. In this talk, I’ll introduce some major research works in this field. It will cover the popular infinite-to-one mapping-based cancelable biometrics template design, Attack via Record Multiplicity (ARM), ARM attack resilient cancelable biometrics designs, and hill-climbing attacks on biometrics templates. We will introduce our latest projects/works on ARM and hill-climbing resilient cancelable deep learning models, and Biometrics-Based Authenticated Key Exchange Protocol with Multi-Factor Fuzzy Extractor (if time permits).