Pair-ID: A Dual Modal Framework for Identity Preserving Image Generation

The acquisition of large-scale paired visible and thermal images is crucial for enhancing face recognition systems, especially in low-light environments where visible spectrum images fail. However, the task is hindered by the scarcity of thermal images and the need for identity consistency during image generation. In this paper, we propose Pair-ID, an innovative framework that addresses these challenges by creating a shared latent space for simultaneous generation of paired visible and thermal images. Pair-ID integrates identity information into text embeddings and employs fixed templates for diverse facial poses, streamlining the customization process and reducing computational demands. The framework's Joint Learner encodes both modalities, facilitating synchronized image generation and preserving facial details. Extensive evaluations show that Pair-ID surpasses current methods in efficiency and performance for paired data generation, making it a promising solution for face recognition under varying lighting conditions.