The use of human biometrics is becoming widespread and its major application is human recognition for controlling unauthorized access to both digital services and physical localities. However, the practical deployment of human biometrics for recognition poses a number of challenges, such as template storage capacity, computational requirements, and privacy of biometric information. These challenges are important considerations, in addition to performance accuracy, especially for authentication systems with limited resources. In this paper, we propose a wave atom transform (WAT)-based palm-vein recognition scheme. The scheme computes, maintains, and matches palm-vein templates with less computational complexity and less storage requirements under a secure and privacy-preserving environment. First, we extract palm-vein traits in the WAT domain, which offers sparser expansion and better capability to extract texture features. Then, the randomization and quantization are applied to the extracted features to generate a compact, privacy-preserving palm-vein template. We analyze the proposed scheme for its performance and privacy-preservation. The proposed scheme obtains equal error rates (EERs) of 1.98%, 0%, 3.05%, and 1.49% for PolyU, PUT, VERA and our palm-vein datasets, respectively. The extensive experimental results demonstrate comparable matching accuracy of the proposed scheme with a minimum template size and computational time of 280 bytes and 0.43 s, respectively.

中文翻译：

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用于基于手掌静脉的人类识别的轻量级且保护隐私的模板生成


人体生物识别技术的使用越来越广泛，其主要应用是人类识别，用于控制对数字服务和物理位置的未经授权的访问。然而，用于识别的人体生物识别技术的实际部署带来了许多挑战，例如模板存储容量、计算要求和生物识别信息的隐私。除了性能准确性之外，这些挑战也是重要的考虑因素，特别是对于资源有限的身份验证系统。在本文中，我们提出了一种基于波原子变换（WAT）的手掌静脉识别方案。该方案在安全和隐私保护的环境下以较低的计算复杂度和较少的存储需求来计算、维护和匹配手掌静脉模板。首先，我们在 WAT 域中提取手掌静脉特征，这提供了更稀疏的扩展和更好的提取纹理特征的能力。然后，将随机化和量化应用于提取的特征，以生成紧凑的、保护隐私的手掌静脉模板。我们分析了所提出的方案的性能和隐私保护。该方案在 PolyU、PUT、VERA 和我们的手掌静脉数据集上分别获得了 1.98%、0%、3.05% 和 1.49% 的等错误率 (EER)。大量的实验结果表明，所提出的方案在最小模板大小和计算时间分别为 280 字节和 0.43 秒的情况下具有相当的匹配精度。