[09/20/2024] Check out the photos at the Gallery!
[09/08/2024] Deadline for poster spotlight presentation is September 08, 2024 at 11:59 PM Pacific Time
[08/13/2024] Technical program is now available
[07/29/2014] Camera-ready submission for accepted papers is now enabled. Please refer to the instructions here. Deadline for camera-ready submission and IEEE copyright submission is August 02, 2024 11:59 AM Pacific Time (not PM).
[07/28/2024] List of accepted papers is now available.
[07/23/2024] Authors will be notified of paper decision on July 28, 2024. Refer to Dates for more details.
[06/10/2024] Use the submission site https://cmt3.research.microsoft.com/GENAI2024 Refer to Dates and Guidelines for details.
Generative AI for Futuristic Biometrics (GENAI2024) is a Special Session organized as part of International Joint Conference in Biometrics (IJCB 2024) and will explore the potential of generative AI in developing different proactive defensive and reactive attack detection strategies to secure the future of biometrics. The special session will foster collaboration between researchers, practitioners, and industry experts to encourage novel methodologies, explore new applications, and identify challenges associated with generative AI in face, fingerprint, iris, voice, gait and other behavioral modalities.
Motivation
Generative AI has significantly reshaped modern machine learning in both vision and language domains in terms of unprecedented realism, diversity and efficiency. This will also have an impact on biometric research that heavily relies on large-scale, diverse, sensitive and personally identifiable data. On one hand, we can leverage generative models for controllable synthesis of large amounts of biometric data in an efficient automated fashion. This synthetic data in turn can be used for de-biasing existing models or extending data when real data is limited. This is essential, specifically, in extreme cases such as post-mortem-based recognition or recognition of infants and children, where data collection is significantly restricted or nearly impossible. Another potential use of generative models can be adaptation of text-driven large language models to produce natural language interpretation of data. The generated descriptions can explain the decisions generated by the biometric systems thereby, making them more trustworthy and explainable. On the other hand, generative AI can be used in an adversarial capacity to circumvent existing systems. Novel attack vectors such as spoofs, template inversion and Deepfake can be simulated more effectively using generative AI. Biometrics of the future should therefore, utilize this novel potential of generative AI to both identify vulnerabilities in existing systems and develop intelligent, trustworthy and robust systems.
Topics of interest include, but are not limited to the following list.
Novel generative models for image and voice generation
Generative models for biometrics in the extreme (post-mortem, young children, etc)
Generative models for attack vectors (sensor and template level attacks)
Text-based generative models for explainable biometrics
Evaluation and human perception of generated data
Evaluation of generative models for diversity and controllability
Novel applications of generative AI in biometrics