论文标题
MLP-HASH：通过随机多层感知器的哈希保护面部模板
MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron
论文作者
论文摘要
面部识别系统用于身份验证目的的应用正在迅速增长。尽管最新的（SOTA）面部识别系统具有很高的识别精度，但为每个用户提取并存储在系统数据库中的功能包含对隐私敏感的信息。因此，损害此数据将危害用户的隐私。在本文中，我们提出了一种称为MLP-HASH的新的可取消模板保护方法，该方法通过通过特定于用户特定的随机加权多层多层感知器（MLP）传递提取的功能来生成受保护的模板，并对MLP输出进行二合作。我们评估了我们提出的生物识别模板保护方法的无键，不可逆性和识别精度，以满足ISO/IEC 30136标准要求。我们在Mobio和LFW数据集上对SOTA面部识别系统进行的实验表明，我们的方法具有竞争性能，具有生物损坏和IOM HASHINing（IOM-GRP和IOM-IM-EURP）模板保护算法。我们提供了本文提出的所有实验的开源实施，以便其他研究人员可以验证我们的发现并在我们的工作基础上进行基础。
Applications of face recognition systems for authentication purposes are growing rapidly. Although state-of-the-art (SOTA) face recognition systems have high recognition accuracy, the features which are extracted for each user and are stored in the system's database contain privacy-sensitive information. Accordingly, compromising this data would jeopardize users' privacy. In this paper, we propose a new cancelable template protection method, dubbed MLP-hash, which generates protected templates by passing the extracted features through a user-specific randomly-weighted multi-layer perceptron (MLP) and binarizing the MLP output. We evaluated the unlinkability, irreversibility, and recognition accuracy of our proposed biometric template protection method to fulfill the ISO/IEC 30136 standard requirements. Our experiments with SOTA face recognition systems on the MOBIO and LFW datasets show that our method has competitive performance with the BioHashing and IoM Hashing (IoM-GRP and IoM-URP) template protection algorithms. We provide an open-source implementation of all the experiments presented in this paper so that other researchers can verify our findings and build upon our work.
