用于可靠用户验证的合成前额皱纹生物特征生成
Synthetic Forehead-creases Biometric Generation for Reliable User Verification
AI总结：
本文提出一种基于布朗桥扩散模型的前额皱纹图像合成框架，包含特定主体和非特定主体生成模块，生成具有独特性和真实性的合成数据，有效提升了前额皱纹验证系统的准确率。
摘要：
近期研究强调了前额皱纹模式作为人脸、虹膜和眼周识别替代方案的潜力，提供了非接触式和便捷的解决方案，特别是在面部被外科口罩遮挡的情况下。然而，收集前额数据面临挑战，包括成本和时间限制，因为开发和优化前额验证方法需要大量高质量图像。为了应对这些挑战，合成生物特征数据的生成因其能够在保护隐私的同时实现基于深度学习的生物特征验证方法的有效训练而受到关注。在本文中，我们提出了一个新的框架来合成前额皱纹图像数据，同时保持重要特征，如独特性和真实性。所提出的框架由两个主要模块组成：基于图像到图像布朗桥扩散模型（BBDM）的特定主体生成模块（SSGM），该模块学习图像对之间的多对一映射以生成与真实主体对应的身份感知合成前额皱纹；以及非特定主体生成模块（SAGM），该模块在SSGM的辅助下采样新的合成身份。我们主要使用Fréchet Inception Distance（FID）和结构相似性指数测度（SSIM）评估生成的前额皱纹图像的多样性和真实性。此外，我们使用前额皱纹验证系统（FHCVS）评估合成生成的前额皱纹图像的实用性。结果表明，利用合成数据提高了FHCVS的验证准确率。
英文摘要：
Recent studies have emphasized the potential of forehead-crease patterns as an alternative for face, iris, and periocular recognition, presenting contactless and convenient solutions, particularly in situations where faces are covered by surgical masks. However, collecting forehead data presents challenges, including cost and time constraints, as developing and optimizing forehead verification methods requires a substantial number of high-quality images. To tackle these challenges, the generation of synthetic biometric data has gained traction due to its ability to protect privacy while enabling effective training of deep learning-based biometric verification methods. In this paper, we present a new framework to synthesize forehead-crease image data while maintaining important features, such as uniqueness and realism. The proposed framework consists of two main modules: a Subject-Specific Generation Module (SSGM), based on an image-to-image Brownian Bridge Diffusion Model (BBDM), which learns a one-to-many mapping between image pairs to generate identity-aware synthetic forehead creases corresponding to real subjects, and a Subject-Agnostic Generation Module (SAGM), which samples new synthetic identities with assistance from the SSGM. We evaluate the diversity and realism of the generated forehead-crease images primarily using the Fréchet Inception Distance (FID) and the Structural Similarity Index Measure (SSIM). In addition, we assess the utility of synthetically generated forehead-crease images using a forehead-crease verification system (FHCVS). The results indicate an improvement in the verification accuracy of the FHCVS by utilizing synthetic data.
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