SynAdult: Multimodal Synthetic Adult Dataset Generation via Diffusion Models and Neuromorphic Event Simulation for Critical Biometric Applications
We propose SynAdult, a multimodal synthetic data generation framework designed to address the scarcity of diverse and privacy-compliant senior adult face datasets for biometric applications and facial analysis. Our pipeline begins with the rendering of high-fidelity 2D adult facial images using para...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11106428/ |
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| Summary: | We propose SynAdult, a multimodal synthetic data generation framework designed to address the scarcity of diverse and privacy-compliant senior adult face datasets for biometric applications and facial analysis. Our pipeline begins with the rendering of high-fidelity 2D adult facial images using parameter-efficient LoRA-based tuning of the state-of-the-art hyperrealism Stable Diffusion XL (SDXL) model, producing photorealistic outputs across diverse ethnicities and age-specific features. Next, we integrate a video retargeting pipeline to synthesize temporally consistent head pose and facial expression sequences, ensuring naturalistic dynamics crucial for downstream video-based facial analysis. In the third stage, we generate neuromorphic event data to introduce a privacy-preserving modality aligned with real-world edge deployment scenarios, such as ambient monitoring and in-vehicle sensing, where high temporal resolution and minimal identity leakage are beneficial. Finally, we reconstruct detailed 3D facial meshes from single 2D frames using 2D-to-3D morphing techniques to capture fine-grained structural details. This modality enhances geometric understanding and supports applications in AR/VR and affective computing. To validate the robustness and utility of the generated dataset, we perform a comprehensive evaluation using Kernel Inception Distance (KID), BRISQUE, CLIP score, and identity similarity metrics. We further assess downstream applicability employing the state-of-the-art facial expression classification networks and event facial landmarks tests for downstream machine learning tasks. As a key contribution, we open-source a large-scale, multimodality, multi-race adult dataset, enabling future research in secure and ethically grounded synthetic data for facial biometrics and facial analysis applications. The project website, along with the complete adult multimodality dataset and the fine-tuned model, is available at <uri>https://mali-farooq.github.io/SynAdult/</uri> |
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| ISSN: | 2169-3536 |