Controllable Diffusion Model for Generating Multimodal Biometric Images

High-quality, diversified, and large-scale datasets are crucial for creating reliable deep-learning models for biometric applications. Unfortunately, there is a shortage of well-labeled data. This paper introduces a text-conditional biometric imaging generation framework, addressing the complexities associated with multi-modality considerations. The proposed framework harnesses cutting-edge diffusion probabilistic models to produce multi-modal biometric images at high resolutions, seamlessly aligning with biometric language prompts. The experimental results unequivocally validate the efficacy of the proposed framework in generating a diverse array of highly realistic synthetic biometric images while consistently maintaining a commendable level of fidelity when juxtaposed with their respective reference datasets. The contributions of this study offer substantial potential for propelling advancements in biometric imaging research.