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

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-16 DOI:10.1109/LSP.2024.3461648
Jingyu Lin;Yongrong Wu;Zeyu Wang;Xiaode Liu;Yufei Guo
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Abstract

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.
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Pair-ID:身份保护图像生成的双模框架
获取大规模成对的可见光和热图像对于增强人脸识别系统至关重要,尤其是在可见光光谱图像失效的弱光环境中。然而,热图像的稀缺性和图像生成过程中身份一致性的要求阻碍了这项任务的完成。在本文中,我们提出了 Pair-ID 这一创新框架,通过创建一个共享潜空间来同时生成成对的可见光和热图像,从而应对这些挑战。Pair-ID 将身份信息整合到文本嵌入中,并针对不同的面部姿势采用固定模板,从而简化了定制过程并降低了计算需求。该框架的联合学习器同时对两种模式进行编码,便于同步生成图像并保留面部细节。广泛的评估表明,Pair-ID 在配对数据生成的效率和性能方面超越了当前的方法,使其成为在不同光照条件下进行人脸识别的理想解决方案。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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