The Experts below are selected from a list of 2523 Experts worldwide ranked by ideXlab platform

Nesterowicz Vanessa - One of the best experts on this subject based on the ideXlab platform.

  • Deep Face Fuzzy Vault: Implementation and Performance
    2021
    Co-Authors: Rathgeb Christian, Merkle Johannes, Scholz Johanna, Tams Benjamin, Nesterowicz Vanessa
    Abstract:

    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

  • Deep Face Fuzzy Vault: Implementation and Performance
    2021
    Co-Authors: Rathgeb Christian, Merkle Johannes, Scholz Johanna, Tams Benjamin, Nesterowicz Vanessa
    Abstract:

    Biometric technologies, especially face recognition, have become an essential part of identity management systems worldwide. In deployments of Biometrics, secure storage of Biometric information is necessary in order to protect the users' privacy. In this context, Biometric cryptosystems are designed to meet key requirements of Biometric information protection enabling a privacy-preserving storage and comparison of Biometric data. This work investigates the application of a well-known Biometric cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors extracted through deep convolutional neural networks. To this end, a feature transformation method is introduced which maps fixed-length real-valued deep feature vectors to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted. 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. In experiments, an unlinkable improved deep face fuzzy vault-based template protection scheme is constructed employing features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). 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 an effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face

V Sujitha - One of the best experts on this subject based on the ideXlab platform.

  • security analysis of prealigned fingerprint template using fuzzy vault scheme
    Cluster Computing, 2019
    Co-Authors: D Chitra, V Sujitha
    Abstract:

    Biometric systems accumulate information from Biometric elements of an individual and used to exceptionally confirm the person with the physical and behavioral properties of the Biometric Characteristic. Biometrics and cryptography systems have been identified as the two main components of digital security system. Cryptography is combined with Biometric to achieve high security. In this paper, fingerprint template protection is proposed which is based on fuzzy vault scheme. Initially, enrolled fingerprint images are preprocessed using some image processing techniques and preprocessed images are prealigned automatically using directed reference point. All the minutiae points are extracted and extracted minutiae features along with secret key are used to produce the fuzzy vault. Query features are given as an input with the stored template to recover the corresponding key. The proposed method is validated on FVC 2002 database. Simulation results prove that the proposed method can achieve high genuine acceptance rate with improved security.

Rathgeb Christian - One of the best experts on this subject based on the ideXlab platform.

  • Deep Face Fuzzy Vault: Implementation and Performance
    2021
    Co-Authors: Rathgeb Christian, Merkle Johannes, Scholz Johanna, Tams Benjamin, Nesterowicz Vanessa
    Abstract:

    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

  • Deep Face Fuzzy Vault: Implementation and Performance
    2021
    Co-Authors: Rathgeb Christian, Merkle Johannes, Scholz Johanna, Tams Benjamin, Nesterowicz Vanessa
    Abstract:

    Biometric technologies, especially face recognition, have become an essential part of identity management systems worldwide. In deployments of Biometrics, secure storage of Biometric information is necessary in order to protect the users' privacy. In this context, Biometric cryptosystems are designed to meet key requirements of Biometric information protection enabling a privacy-preserving storage and comparison of Biometric data. This work investigates the application of a well-known Biometric cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors extracted through deep convolutional neural networks. To this end, a feature transformation method is introduced which maps fixed-length real-valued deep feature vectors to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted. 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. In experiments, an unlinkable improved deep face fuzzy vault-based template protection scheme is constructed employing features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). 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 an effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face

D Chitra - One of the best experts on this subject based on the ideXlab platform.

  • security analysis of prealigned fingerprint template using fuzzy vault scheme
    Cluster Computing, 2019
    Co-Authors: D Chitra, V Sujitha
    Abstract:

    Biometric systems accumulate information from Biometric elements of an individual and used to exceptionally confirm the person with the physical and behavioral properties of the Biometric Characteristic. Biometrics and cryptography systems have been identified as the two main components of digital security system. Cryptography is combined with Biometric to achieve high security. In this paper, fingerprint template protection is proposed which is based on fuzzy vault scheme. Initially, enrolled fingerprint images are preprocessed using some image processing techniques and preprocessed images are prealigned automatically using directed reference point. All the minutiae points are extracted and extracted minutiae features along with secret key are used to produce the fuzzy vault. Query features are given as an input with the stored template to recover the corresponding key. The proposed method is validated on FVC 2002 database. Simulation results prove that the proposed method can achieve high genuine acceptance rate with improved security.

Javier Ortegagarcia - One of the best experts on this subject based on the ideXlab platform.

  • Biometric presentation attack detection beyond the visible spectrum
    IEEE Transactions on Information Forensics and Security, 2020
    Co-Authors: Ruben Tolosana, Christoph Busch, Marta Gomezbarrero, Javier Ortegagarcia
    Abstract:

    The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of Biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to Biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake Biometric Characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on $i$ ) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and $ii$ ) an in-depth analysis of several state-of-the-art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).

  • Biometric presentation attack detection beyond the visible spectrum
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Ruben Tolosana, Christoph Busch, Marta Gomezbarrero, Javier Ortegagarcia
    Abstract:

    The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of Biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to Biometric systems. Among them, Presentation Attacks (PAs, i.e., attempts to log into the system with a fake Biometric Characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. The Biometrics community has thus made a considerable effort to the development of automatic Presentation Attack Detection (PAD) mechanisms, for instance through the international LivDet competitions. In this context, we present a novel fingerprint PAD scheme based on $i)$ a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and $ii)$ an in-depth analysis of several state-of-the-art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.36\% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).