Center for Identification Technology Research
National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC).
Our mission is to work in partnership with our government and industry stakeholders to advance the state of the art in human identification capabilities through coordinated university research.
CITeR is a unique cooperative model which addresses industry and government challenges and provides an over 20 times return on investment. University of North Carolina – Charlotte is excited to join CITeR as a University Site (pending NSF approval).
– Stephanie Schuckers, director of CITeR
Key Research Areas:
IDENTITY, BIOMETRIC, EXPLAINABLE AI, VULNERABILITIES,
GENERATIVE AI, TRANSPARENCY, SECURITY
Research Highlights
Advancing Noncontact Fingerprint Recognition
Noncontact fingerprint recognition is poised for growth given the cost, hardware requirements and logistical challenges of traditional fingerprint systems. The pervasiveness of cell phone ownership propels exploration of noncontact fingerprint in certain use cases. CITeR Researchers are researching in this area, addressing contactless fingerprint interoperability with legacy contact fingerprints and evaluating potential sources of differential performance.
Behavioral
Biometrics
Recognition based on wearable behavioral biometrics is an emerging alternative to camera-based systems, and uses sensors such as radar, accelerometer and other signals. This approach ensures privacy, provides 3D sensing, and operates in challenging and alternate conditions. CITeR researchers are active in the development of behavioral algorithms and datasets.
Detection of
Deepfakes
In the era of Artificial Intelligence, Deepfake technology has become
one of the major threats to privacy, creativity, and authenticity. Deepfakes have extended influence across multiple domains, involving the manipulation of texts, audio, videos, images, and political and creative content. With growing concerns regarding the threats of
deepfakes, CITeR researchers are developing ways to detect and prevent manipulated content.
Face
Morphing
With the advent of advanced generative AI, the vulnerability of
merging of two faces into a single image has surfaced. The result is
that two distinct individuals both match a single morphed image and
are able to share an identity, which is potentially disruptive for digital
identity systems. CITeR researchers are advancing from two sides. From the attackers side we are creating sophisticated databases of high quality morphs. From the protection side we are creating morph detection algorithms that detect a variety of morphs.
Presentation
Attack Detection
Presentation attacks are a prevalent security concern today, where impostors attempt to gain access to restricted resources using fake biometric data such as face, fingerprint, or iris images. To mitigate these attacks, various presentation attack detection (PAD) systems have been deployed, often leveraging deep learning models for their high detection accuracy. CITeR researchers are working on creation of new spoof attacks, development of new PAD methods, and evaluation of these methods through open competitions and new datasets for development purposes.
Privacy and Security
in Biometrics
Protecting sensitive biometric data at scale will be critical for gaining public trust, achieving large-scale deployment, and ensuring regulatory compliance with privacy laws. CITeR is studying the security and privacy aspects of privacy-enhancing technologies (PETs) that have been developed specifically for biometrics. PETs aim to protect sensitive personal data while maintaining the functionality of identity management, such as homomorphic encryption HE , and secure multi-party computation MPC .
Faculty
Heather Lipford, co-director CITeR
Software and Information Systems
Usable Security and Privacy, Human-Computer Interaction, and social computing
co-director, HCI Lab
Member of the UNC Charlotte Cyber Defense and Network Assurability Center.
Stephanie Schuckers, co-director CITeR
Computing & Informatics
Bank of America distinguished professor
Biometrics, machine learning, security, image processing
IEEE Fellow
Board of Directors, Biometrics Institute
President-Elect, IEEE Biometrics Council
Liyue Fan
Computer Science
Privacy-preserving methods for computer vision, behavioral, and health data
Theoretically well-grounded privacy models such as differential privacy
NSF CAREER Awardee
Srijan Das
Computer Science
assistant professor
Video Representation Learning, and Robotic Vision
Charlotte Machine Learning Lab (CharMLab) and AI4Health Center
Pu Wang
Computer Science
associate professor
Deep learning and reinforcement learning, with applications in smart sensing, networking, computer vision, Internet of Things, and Cyber-Physical Systems
Lance Peterman
Software and Information Systems
adjunct faculty
Cybersecurity, focusing on identity & access management
Teaches Identity Management at UNC Charlotte
Bojan Cukic
Computing and Informatics
dean
Former Director of CITeR
Information assurance and biometrics, software engineering with emphasis on verification and validation, and resilient computing
Michael Schuckers
Software and Information Systems
professor
Statistical methodologies for bio-authentication technologies such as facial recognition and fingerprint readers
Fulbright Scholar