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
*********************************************************************************************
*** 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
Synthetic Data for Face and Gesture]: Call for Papers – held in conjunction with IEEE FG 2025
March 18th, 2025 
Daniela Lopez de Luise Held in the scope of IEEE FG 2025
26 or 30 May 2025 (TBD), Clearwater, Florida, USA
https://sites.google.com/view/sd-fga-2025/home
Paper submission: 9 April 2025, 11:59pm PST
*****************************
*** Call for Papers ***
Recent advancements in generative models within the realms of computer
vision and artificial intelligence have revolutionized the way
researchers approach data-driven tasks. The advent of sophisticated
generative models, such as GANs (Generative Adversarial Networks), VAEs
(Variational Autoencoders), or more recently diffusion models, has
empowered practitioners to create synthetic data that closely mirrors
real-world scenarios. These models enable the generation of
high-fidelity images and sequences, laying the foundation for
groundbreaking applications in face and gesture analysis. The
significance of these generative models lies in their ability to produce
synthetic data that is remarkably realistic, thereby mitigating
challenges associated with data scarcity and privacy concerns. As a
result, the utilization of synthetic data has become increasingly
prevalent in various research domains, offering a versatile and ethical
alternative for training and testing machine learning algorithms.
This workshop aims to delve into the diverse applications of synthetic
data in the realm of face and gesture analysis. Participants will
explore how synthetic datasets have been instrumental in training facial
recognition systems, enhancing emotion detection models, and refining
gesture recognition algorithms. The workshop will showcase exemplary use
cases where the integration of synthetic data has not only overcome data
limitations but has also fostered the development of more robust and
accurate models. As researchers increasingly recognize the potential of
synthetic datasets in shaping the future of computer vision and machine
learning, there arises a demand for a collaborative platform where ideas
can be exchanged, methodologies shared, and challenges addressed. This
workshop aims to bridge the gap between theoretical knowledge and
practical implementation, fostering a community of experts and
enthusiasts dedicated to advancing the frontiers of synthetic data in
face and gesture analysis.
Topics of interest include, but are not limited to:
+ Novel generative techniques for producing realistic face and gesture data
+ Innovative approaches for labeling and annotating synthetic data
+ Methods for preventing data leakage in synthetic datasets
+ Development of synthetic data pipelines for biometrics
+ Techniques for using synthetic data to enrich and augment existing
datasets
+ Synthetic data as a tool for bias reduction and promoting fairness in
face and gesture analysis
+ Criteria and methodologies for assessing the quality of synthetic
datasets
+ Privacy-focused synthetic data generation for sensitive applications
+ New applications for synthetic data in areas like augmented reality,
animation, and virtual environments
+ Comparative performance benchmarks and quality assessments of
synthetic datasets
*** Paper format and submission ***
International Joint Conference on Biometrics (IJCB) 2025 – Submission Deadline: 11 April 2025
March 18th, 2025 Daniela Lopez de Luise https://ijcb2025.ieee-biometrics.org/
Paper submission: 11 April 2025, 11:59pm PST
*****************************
*** About IJCB 2025 ***
The IEEE International Joint Conference on Biometrics (IJCB) is the
premier international forum for research in biometrics and related
technologies. It combines two major biometrics conferences, the IEEE
Biometrics Theory, Applications, and Systems (BTAS) conference and the
International Conference on Biometrics (ICB), and is made possible
through a special agreement between the IEEE Biometrics Council and the
IAPR TC-4. IJCB 2025 is the 9th iteration of this major joint event and
will be held in Osaka, Japan between 8-11 September 2025 as an in-person
conference.
*** Call for Contributions ***
IJCB 2025 is intended to have a broad scope and invites papers that
advance biometric technologies, sensor design, feature extraction and
matching algorithms, security and privacy, and social impact of
biometrics technology. Topics of interest include, but are not limited to:
+ Face, Iris, Fingerprint, Palmprint
+ Periocular, Ear, Vein, Speech
+ Gait, Gesture and Action Recognition
+ Multi-modal and Multi-Spectral Biometrics
+ Mobile-based Biometrics
+ Template Protection and Cryptosystems
+ Privacy, Demographic Bias, Fairness
+ Biometrics Explainability and Interpretability
+ Template Design, Selection and Update
+ Datasets, Evaluation, Benchmarking
+ Performance Modelling and Prediction
+ Large scale ID Management
+ Anti-spoofing, Presentation Attack Detection
+ Biometric DeepFakes, Digital Data Forensics
+ Biometric-related Law Enforcement and Forensics
+ Biometrics in Healthcare, Banking, IoT
+ Biometric-related Synthetic Realities
+ Ethical, Social and Legal Issues
***Paper Submission ***
Submitted papers may not be accepted or under review elsewhere.
Submissions may be up to eight pages, plus additional references, in
conference format. Please visit the submission page for additional
details on paper formatting. Accepted papers will be submitted for
inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and
quality requirements.
Submission is through CMT: https://cmt3.research.microsoft.com/IJCB2025.
*** Important Dates ***
+ Abstract and paper submission deadline: April 11, 2025 (11:59pm Pacific)
+ Decisions to authors: July 3, 2025
+ Camera-ready submission: July 25, 2025
*** Awards and TBIOM Special Issue ***
Several awards will be given out to the best papers from IJCB 2025,
including (1) the best paper award, (2) the best student paper award,
(3) daily best poster awards. The awards will consist of a commemorative
plaque as well as award money.
Additionally, the authors of the best-reviewed papers will be invited to
submit an extended version of their papers to a special issue of the
IEEE Transactions on Biometrics, Behavior, and Identity Science
(IEEE-TBIOM).
Contact PC: ijcb2025pcs@googlegroups.com
Ingeniería Básica en Instrumentación & Control – Últimos días de inscripción con descuento!
March 18th, 2025 Daniela Lopez de Luise |
ÚLTIMOS DÍAS DE INSCRIPCIÓN CON DESCUENTO!!!
Consultas a cursos@aadeca.org o por whatsapp 011 3201-2325 |
.png)
Call for Participation – SHREC 2025: Retrieval and Segmentation of Multiple Relief Patterns
March 18th, 2025 Daniela Lopez de Luise Relief patterns (or geometric textures) can be defined as a repeated pattern of local surface corrugation, independent of the global shape of the object or the photometric properties of the surface.
The analysis of relief patterns has applications in various fields requiring the characterization of regular (or quasi-regular) structures, such as fabric inspection, medical imaging, and the restoration of historic artifacts.
- Pattern retrieval: Given a query mesh, participants are required to identify whether and where the relief patterns present in the query are located on the surface of a 3D model from a retrieval dataset.
- Optional challenge – Pattern segmentation: Participants are required to segment the different relief patterns present on the surface of 3D models in the query dataset.