Deep convolutional neural networks have achieved remarkable improvements in facial recognition performance. Similar kinds of developments, e.g. deconvolutional neural networks, have shown impressive results for reconstructing face images from their corresponding embeddings in the latent space. This poses a severe security risk which necessitates the protection of stored deep face embeddings in order to prevent from misuse, e.g. identity fraud. In this work, an unlinkable improved deep face fuzzy vault-based template protection scheme is presented. To this end, a feature transformation method is introduced which maps fixed-length real-valued deep face embeddings to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted using features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). At key binding, obtained feature sets are locked in an unlinkable improved fuzzy vault. For key retrieval, the efficiency of different polynomial reconstruction techniques is investigated. The proposed feature transformation method and template protection scheme are agnostic of the biometric characteristic and, thus, can be applied to virtually any biometric features computed by a deep neural network. For the best configuration, a false non-match rate below 1% at a false match rate of 0.01%, is achieved in cross-database experiments on the FERET and FRGCv2 face databases. On average, a security level of up to approximately 28 bits is obtained. This work presents the first effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face.
翻译:深相神经网络在面部识别性表现方面已经取得了显著的改善。类似类型的发展,例如,分相神经网络,已经显示了从潜潜藏空间的相应嵌入中重建面部图像的令人印象深刻的结果。这构成了严重的安全风险,需要保护存储的深面嵌入,以防止滥用,例如身份欺诈。在这项工作中,提出了一张无法连接的、面部更深、模糊的保险库模板保护计划。为此,采用了一种特征转换方法,绘制固定长度、真实面部更深值的嵌入全价特征数据集。作为上述特征转换的一部分,对不同特征的量化和二进化技术进行详细分析,使用先进的深度面部嵌嵌入面部嵌入式嵌入器进行提取,通过添加角边际差损失(ArcFace)来培训。在关键绑定、获得的特征组被锁定在一个无法连接的改进的模糊保险库中。对于关键面部、不同多面面层重建技术的效率进行了调查。关于面面面面部和面面面面面面面面部重建技术的效率,作为上述功能转换的一部分,作为部分转换的一部分,对不同特征进行详细分析,利用了不同特征变压的图像保护方法,因此,在数据库中,将一个数据库中,将一个虚拟的基流数据转换为一个数据库中,一个不动的模型的模型系统,一个虚拟的模型系统将显示一个不动的系统。