Generative AI and Large Vision-Language Models for Biometrics Call for Papers

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CALL FOR PAPERS

IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM)
Special Issue on
Generative AI and Large Vision-Language Models for Biometrics

Submission Deadline: 31 May 2025
Targeted Publication: Q1 2026

Paper submission: https://ieee.atyponrex.com/journal/tbiom

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*** Motivation ***

In the rapidly advancing field of artificial intelligence, generative
AI and large-scale vision-language models are becoming key areas of
interest, revolutionizing numerous research fields, including natural
language processing and computer vision. Generative AI models are
designed and trained to approximate the underlying distribution of a
dataset, enabling the generation of new samples that reflect the
patterns and regularities within the training data. Among the various
types of generative models, such as Generative Adversarial Networks
(GANs), Variational Autoencoders (VAEs), flow-based, autoregressive,
and diffusion models, GANs and diffusion models have gained
significant attention and are widely applied to tasks such as image
synthesis, image manipulation, text generation, and speech
synthesis. These models have shown remarkable success in modeling and
interpreting the probability distributions of real-world
data. Vision-language models, on the other hand, integrate visual and
textual data, learning to associate these modalities to enhance
understanding and enable multimodal reasoning-based applications.

The advancements in generative AI and vision-language models (LVMs)
are also making a significant impact on biometrics, offering new
possibilities for addressing longstanding challenges. Generative AI,
with its ability to synthesize highly realistic data, has the
potential to address privacy concerns related to collecting, sharing,
and using sensitive biometric data. This synthetic data can also be
used to increase diversity and variation in training datasets through
augmentation, thus improving model generalizability and reducing
potential bias induced by imbalanced training data. At the same time,
large vision-language models offer the capability to process and
understand multimodal information by combining visual features with
contextual data, such as semantic insights from natural
language. Furthermore, large-scale vision-language models can be
optimized for downstream tasks, such as template extraction, using
zero or few-shot learning approaches, making them highly versatile for
biometric applications.

Although generative AI and vision-language models offer a rich set of
tools that can be utilized to address challenges in biometrics, the
misuse of these technologies presents a threat to the
field. Generative AI models have the ability to incorporate conditions
in the generation process to take control over the generated
samples. This enables a wide range of applications such as
image-to-image translation, text-to-image synthesis, and style
transfer. However, this capability also allows for creating deepfake
attacks, e.g., images, videos, and audio that are indistinguishable or
nearly indistinguishable from real content. The increased realism and
widespread public accessibility of generative AI have raised concerns
about the potential misuse of this technology for malicious
purposes. This highlights the need for solutions to detect generated
AI content and mitigate the potential misuse of generative AI models.

The proposed TBIOM special issue will provide a platform to discuss
the latest advancements and technical achievements related to
Generative AI and Large vision-language models when applied to
problems in biometrics. The topics of interest of the special issue
include, but are not limited to:

+ Novel generative AI models for responsible synthesis of biometric data
+ Novel generative models for conditional data synthesis
+ Biometrics interpretability and explainability through large
  language-vision models
+ Few-shot learning from large language-vision models
+ Generative AI and LVMs for detecting attacks on biometrics systems
+ Generative AI-based image restoration
+ Information leakage of synthetic data
+ Data factories and label generation for biometric models
+ Quality assessment of AI generated data
+ Synthetic data for data augmentation
+ Detection of generated AI contents
+ Bias mitigation using synthetic data
+ LLMs and VLMs for biometrics
+ Watermarking AI generated content
+ New synthetic datasets and performance benchmarks

+ Security and privacy issues regarding the use of generative AI
methods for biometrics

+ Ethical considerations regarding the use of generative AI methods
for biometrics

+ Parameter efficient fine-tuning of VLMs for biometrics applications


*** Important Dates ***

Submission deadline:                          31 May 2025
First round of reviews completed (first decision):         August 2025
Second round of reviews completed                 October 2025
Final papers due                        December 2025
Publication date:                         Q1 2026


*** Paper Submission ***

Papers should be submitted through the TBIOM submission portal before
the deadline using the TBIOM journal templates:
https://ieee.atyponrex.com/journal/tbiom and selecting the article
type: "Generative AI and Large Vision-Language Models for
Biometrics".


*** Guest Editors: ***

+ Fadi Boutros, Fraunhofer IGD, Germany
+ Hu Han, Institute of Computing Technology, Chinese Academy of Sciences (CAS), China
+ Tempestt Neal, University of South Florida, United States
+ Vishal M. Patel, Johns Hopkins University, United States
+ Vitomir Štruc, University of Ljubljana, Slovenia
+ Yunhong Wang, Beihang University, China