Call for Papers

We look forward to welcoming everyone in Clearwater, FL, USA May 26-30
for FG2025! We have three exciting keynotes from Stephanie Schuckers,
Kevin Bowyer, and Jonathan Gratch. Along with this, we will have an
Ask Me Anything (AMA) panel with all three keynotes! The AMA is
tentatively scheduled to take place on May 28 from 2-3pm.

 

Stephanie Schuckers

Dr. Stephanie Schuckers is the Bank of America Distinguished Professor
in Computing & Informatics at University of North Caroline (UNC) –
Charlotte. She also serves as the Director of the Center for
Identification Technology Research (CITeR), a National Science
Foundation Industry/University Cooperative Research Center, led by
Clarkson University. She received her doctoral degree in Electrical
Engineering from The University of Michigan. Professor Schuckers
research focuses on processing and interpreting signals which arise
from the human body. Her work is funded from various sources,
including National Science Foundation, Department of Homeland
Security, and private industry, among others. She has started her own
business, testified for US Congress, and has over 50 journal
publications as well as over 100 other academic publications. She was
named IEEE Fellow in 2023, serves as a Board of Directors for the
Biometrics Institute, and is President-Elect for the IEEE Biometrics
Council. She has volunteered for numerous organizations including the
IEEE Biometrics Council and FIDO Alliance.

 

Title. Security, Fairness, and Stability in Biometric Recognition: Challenges and Opportunities

 

Abstract. Biometric recognition has become an everyday part of life
with applications from mobile devices to air travel to finance. There
are two critical functions of biometric recognition (1) “matching”
or determining if a sample matches a previous enrollment and (2)
“presentation attack detection” or determining if the sample
comes from a live individual present at the time of capture. Attacks
include physical artefacts such as printouts, image/video display, or
masks. More recently, with the leap in deepfake generation tools,
digital injection attacks have become a more frequently exploited
vulnerability. Injection attack detection (IAD) are a combination of
cybersecurity protections (e.g. virtual camera detection) and
biometric-related solutions, such as challenge response and deepfake
detection. Studies have shown that errors related to PAD/IAD often
overwhelm matching errors, suggesting that more research and
evaluation are needed. This talk gives an overview of the field,
discusses related research in fairness and explainability, and
presents a longitudinal study of faces in children.



Kevin Bowyer

Kevin Bowyer is the Schubmehl-Prein Family Professor of Computer
Science and Engineering at the University of Notre Dame. Professor
Bowyer was elected as a Fellow of the American Academy for the
Advancement of Science “for distinguished contributions to the
field of computer vision and pattern recognition, biometrics, object
recognition and data science”, a Fellow of the IEEE “for
contributions to algorithms for recognizing objects in images”, and
a Fellow of the IAPR “for contributions to computer vision, pattern
recognition and biometrics”. He has received a Technical
Achievement Award from the IEEE Computer Society “for pioneering
contributions to the science and engineering of biometrics”, and
IEEE Biometrics Council’s Meritorious Service Award and Leadership
Award. Professor Bowyer has served as Editor-In-Chief of both the IEEE
Transactions on Biometrics, Behavior, and Identity Science and the
IEEE Transactions on Pattern Analysis and Machine
Intelligence. Professor Bowyer has served as General Chair or Program
Chair of conferences such as Computer Vision and Pattern Recognition,
Winter Conference on Applications of Computer Vision, and Face and
Gesture Recognition, and is one of the founding General Chairs of the
International Joint Conference on Biometrics conference series.

 

Title. Face Recognition, Demographic Accuracy Variation, and Wrongful Arrest

 

Abstract. This talk first examines the issue of how face recognition
accuracy is different across demographics. Then we consider the common
assumption that demographic accuracy variation arises due to
demographic imbalance in the training data. Finally, we evaluate the
role of automated face recognition in high-profile instances of
wrongful arrest. In each area, empirical evidence may not support some
popular viewpoints.



Jonathan Gratch

Jonathan Gratch is a Research Full Professor of Computer Science and
Psychology at the University of Southern California (USC) and Director
for Virtual Human Research at USC’s Institute for Creative
Technologies. He completed his Ph.D. in Computer Science at the
University of Illinois in Urbana-Champaign in 1995. Dr. Gratch’s
research focuses on computational models of human cognitive and social
processes, especially emotion, and explores these models’ potential
to advance psychological theory and shape human-machine
interaction. He is the founding Editor-in-Chief (retired) of IEEE’s
Transactions on Affective Computing, Associate Editor for Affective
Science, Emotion Review, and former President of the Association for
the Advancement of Affective Computing (AAAC). He is a Fellow of AAAI,
AAAC, and the Cognitive Science Society.

 

Title. A social-functional view on the recognition and analysis of emotional expressions

 

Abstract. Despite consensus among emotion researchers that the social
meaning of emotional expressions is contextual, other-directed,
co-constructed and culturally dependent, computational methods largely
rest on the assumption that expressions denote some internal state
(e.g., emotion or pain) which can be recovered by an expression’s
morphology or timing alone. For example, many papers at the FG
conference adopt a classic detection perspective, in which observers
annotate the presumed internal states revealed by an expression, then
algorithms are trained to predict this mapping without access to the
original context in which the expression was produced. This assumption
is also implicit in government regulations, such as the EU’s AI Act
which bans emotion recognition across many practical. Though social
psychology theirs point to a broader perspective on expressions, they
fail to offer detailed of what constitutes “context” or
“co-construction” to a level that can be exploited by
computational methods. In this talk, I will highlight the use of
automatic expression analysis in social domains, highlighting the
interpersonal processes that shape their production and analysis. I
hope this talk can encourage future work that formalizes the
computational implications of this social perspective, including
clarifying the communicative function of expressions, how are they
shaped and co-constructed via context and culture, and how socially
interactive agents might adapt and engage in meaning creation?

 

Thank you,

FG 2025 General and Program Chairs