[visionlist] IEEE T-BIOM Special Issue on, Generative AI and Large Vision-Language Models for Biometrics

[Vitomir Struc]

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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

-- 
Prof. Vitomir Struc, PhD
Laboratory for Machine Intelligence
Faculty of Electrical Engineering
University of Ljubljana, Slovenija
URL: https://lmi.fe.uni-lj.si/en/vitomir-struc/

VP Technical Activities, IEEE Biometrics Council
Program Co-Chair: IEEE/CVF Winter Conference of Applications in Computer Vision (WACV 2025), https://wacv2025.thecvf.com/
Program Co-Chair: IEEE International Workshop on Information Forensics and Security (WIFS 2024), https://wifs2024.uniroma3.it/
Program Co-Chair: IEEE/IAPR International Joint Conference on Biometrics (IJCB 2025)
General Co-Chair: IEEE/CVF Winter Conference of Applications in Computer Vision (WACV 2026)