Cancelable multi-biometric recognition system based on deep learning

Abdellatef, Essam; Ismail, Nabil A.; Elrahman, Salah Eldin S. E. Abd; Ismail, Khalid N.; Rihan, Mohamed; El‐Samie, Fathi E. Abd

doi:10.1007/s00371-019-01715-5

Cited by 29 publications

(20 citation statements)

References 50 publications

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“…These methods use some sort of algorithmically defined transformation to convert the face embeddings (learned from the input face images) to more secure representations. The proposed transformations include one-way cryptographic [6] or Winner Takes All [7] hashing, convolution of the embedding with a random kernel [8], use of the Fuzzy Commitment [9] or Fuzzy Vault [10] scheme, fusion of a subject's face embedding with a different subject's face embedding using keys extracted from the two sets of features [11], and homomorphic encryption [12]. The main issue with these approaches is that they have not been comprehensively evaluated in terms of their ability to simultaneously satisfy all three properties of face embedding protection methods.…”

Section: Face Embedding Protection Methodsmentioning

confidence: 99%

“…To conduct this analysis, the recognition accuracy of each baseline system (Facenet and Idiap) was first evaluated on the Mobio dataset, by running the mobile0-male verification protocol 7 used to generate the reported baseline results 8 . The same protocol was then applied to the corresponding PolyProtected face verification systems.…”

Section: Recognition Accuracymentioning

confidence: 99%

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Towards Protecting Face Embeddings in Mobile Face Verification Scenarios

Hahn,

Marcel

2021

Preprint

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This paper proposes PolyProtect, a method for protecting the sensitive face embeddings that are used to represent people's faces in neural-network-based face verification systems. PolyProtect transforms a face embedding to a more secure template, using a mapping based on multivariate polynomials parameterised by user-specific coefficients and exponents. In this work, PolyProtect is evaluated on two open-source face verification systems in a mobile application context, under the toughest threat model that assumes a fully-informed attacker with complete knowledge of the system and all its parameters. Results indicate that PolyProtect can be tuned to achieve a satisfactory trade-off between the recognition accuracy of the PolyProtected face verification system and the irreversibility of the PolyProtected templates. Furthermore, PolyProtected templates are shown to be effectively unlinkable, especially if the user-specific parameters employed in the PolyProtect mapping are selected in a non-naive manner. The evaluation is conducted using practical methodologies with tangible results, to present realistic insight into the method's robustness as a face embedding protection scheme in practice. The code to fully reproduce this work is available at: https://gitlab.idiap.ch/bob/bob.paper.polyprotect 2021.

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Section: Face Embedding Protection Methodsmentioning

confidence: 99%

Section: Recognition Accuracymentioning

confidence: 99%

Towards Protecting Face Embeddings in Mobile Face Verification Scenarios

Hahn,

Marcel

2021

Preprint

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“…Abdellatef et al [23] designed a multi-instance CB for the face using multiple CNNs to extract features from multiple regions of a face image, such as face, eyes, nose, mouth, etc. After fusion of several deep features, a cancelable template was generated via bioconvolving encryption.…”

Section: Cancelable Multimodal Biometrics With Deep Learningmentioning

confidence: 99%

Multimodal Biometric Template Protection Based on a Cancelable SoftmaxOut Fusion Network

2022

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Authentication systems that employ biometrics are commonplace, as they offer a convenient means of authenticating an individual’s identity. However, these systems give rise to concerns about security and privacy due to insecure template management. As a remedy, biometric template protection (BTP) has been developed. Cancelable biometrics is a non-invertible form of BTP in which the templates are changeable. This paper proposes a deep-learning-based end-to-end multimodal cancelable biometrics scheme called cancelable SoftmaxOut fusion network (CSMoFN). By end-to-end, we mean a model that receives raw biometric data as input and produces a protected template as output. CSMoFN combines two biometric traits, the face and the periocular region, and is composed of three modules: a feature extraction and fusion module, a permutation SoftmaxOut transformation module, and a multiplication-diagonal compression module. The first module carries out feature extraction and fusion, while the second and third are responsible for the hashing of fused features and compression. In addition, our network is equipped with dual template-changeability mechanisms with user-specific seeded permutation and binary random projection. CSMoFN is trained by minimizing the ArcFace loss and the pairwise angular loss. We evaluate the network, using six face–periocular multimodal datasets, in terms of its verification performance, unlinkability, revocability, and non-invertibility.

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“…Abdelatif et al presented an approach for cancelable face recognition that depends on Convolutional Neural Networks (CNNs) [ 28 , 29 ]. Its idea begins by isolating the face, eyes, nose, and mouth regions.…”

Section: Introductionmentioning

confidence: 99%

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications

Algarni

Banby

Ismail

et al. 2020

Entropy

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The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

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Cancelable multi-biometric recognition system based on deep learning

Cited by 29 publications

References 50 publications

Towards Protecting Face Embeddings in Mobile Face Verification Scenarios

Towards Protecting Face Embeddings in Mobile Face Verification Scenarios

Multimodal Biometric Template Protection Based on a Cancelable SoftmaxOut Fusion Network

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications

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