The Experts below are selected from a list of 15675 Experts worldwide ranked by ideXlab platform
Loris Nanni - One of the best experts on this subject based on the ideXlab platform.
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Combining local, regional and global matchers for a template protected on-line signature verification system
Expert Systems with Applications, 2010Co-Authors: Loris Nanni, Emanuele Maiorana, Alessandra Lumini, Patrizio CampisiAbstract:In this work an on-line signature authentication system based on an ensemble of local, regional, and global matchers is presented. Specifically, the following matching approaches are taken into account: the fusion of two local methods employing Dynamic Time Warping, a Hidden Markov Model based approach where each signature is described by means of its regional properties, and a Linear Programming Descriptor classifier trained by global features. Moreover, a template protection scheme employing the BioHashing and the BioConvolving approaches, two well known template protection techniques for biometric recognition, is discussed. The reported experimental results, evaluated on the public MCYT signature database, show that our best ensemble obtains an impressive equal error rate of 3%, when only five genuine signatures are acquired for each user during enrollment. Moreover, when the proposed protected system is taken into account, the equal error rate achieved in the worst case scenario, that is,when an ''impostor'' is able to steal the hash keys, is equal to 4.51%, whereas an equal error rate ~0 can be obtained when nobody steals the hash keys.
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A novel local on-line signature verification system
Pattern Recognition Letters, 2008Co-Authors: Loris Nanni, Alessandra LuminiAbstract:In this work, an on-line signature verification system based on local information and on a one-class classifier, the Linear Programming Descriptor classifier (LPD), is presented. The information is extracted as time functions of various dynamic properties of the signatures, then the discrete 1-D wavelet transform (WT) is performed on these features. The Discrete Cosine Transform (DCT) is used to reduce the approximation coefficients vector obtained by WT to a feature vector of a given dimension. The Linear Programming Descriptor classifier is trained using the DCT coefficients. Finally, we have studied the fusion among the approach here proposed and the state-of-the-art of the regional, the local and the global approaches. The fusion outperforms all the stand-alone approaches. Results using all the 5000 signatures from the 100 subjects of the SUBCORPUS-100 MCYT Bimodal Biometric Database are presented, yielding remarkable performance improvement both with Random and Skilled Forgeries. We want to stress that our best fusion approach obtains an equal error rate of 5.2% in the Skilled Forgeries, this value is the lowest equal error rate reported in the literature for the SUBCORPUS-100 MCYT.
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A multi-matcher system based on knuckle-based features
Neural Computing and Applications, 2007Co-Authors: Loris Nanni, Alessandra LuminiAbstract:We describe a new multi-matcher biometric approach, using knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon transform and by Haar wavelet, then these features are transformed by non-linear Fisher transform. Finally, the matching process is based on Parzen window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (EER) (near zero equal error rate).
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An improved BioHashing for human authentication
Pattern Recognition, 2007Co-Authors: Alessandra Lumini, Loris NanniAbstract:Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user specific biometric feature (BioHashing) has received much attention. These methods have significant functional advantages over sole biometrics i.e. zero equal error rate. The main drawback of the base BioHashing method proposed in the literature relies in exhibiting low performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we introduce some ideas to improve the base BioHashing approach in order to maintain a very low equal error rate when nobody steals the Hash key, and to reach good performance also when an ''impostor'' steals the Hash key.
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Letters: Multihashing, human authentication featuring biometrics data and tokenized random number: A case study FVC2004
Neurocomputing, 2005Co-Authors: Dario Maio, Loris NanniAbstract:Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user-specific biometric feature has received much attention. These methods have significant functional advantages over solely biometrics, i.e. zero equal error rate. The main drawback of the methods proposed in the literature relies in exhibiting low performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we show that a multimodal fusion, where only one biometric characteristic is combined with the pseudo-random numbers, permits to obtain a zero equal error rate when nobody steals the pseudo-random numbers, and good performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we study the fusion among the score obtained by a Face Recognizer (where the face features are combined with pseudo-random numbers) and the scores of the systems submitted to FVC2004.
Venugopal Vasudevan - One of the best experts on this subject based on the ideXlab platform.
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user evaluation of lightweight user authentication with a single tri axis accelerometer
Human-Computer Interaction with Mobile Devices and Services, 2009Co-Authors: Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, Venugopal VasudevanAbstract:We report a series of user studies that evaluate the feasibility and usability of light-weight user authentication with a single tri-axis accelerometer. We base our investigation on uWave, a state-of-the-art recognition system for user-created free-space manipulation, or gestures. Our user studies address two types of user authentication: non-critical authentication (or identification) for a user to retrieve privacy-insensitive data; and critical authentication for protecting privacy-sensitive data. For non-critical authentication, our evaluation shows that uWave achieves high recognition accuracy (98%) and its usability is comparable with text ID-based authentication. Our results also highlight the importance of constraints for users to select their gestures. For critical authentication, the evaluation shows uWave achieves state-of-the-art resilience to attacks with 3% false positives and 3% false negatives, or 3% equal error rate. We also show that the equal error rate increases to 10% if the attackers see the users performing their gestures. This shows the limitation of gesture-based authentication and highlights the need for visual concealment.
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Mobile HCI - User evaluation of lightweight user authentication with a single tri-axis accelerometer
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services - MobileHCI '09, 2009Co-Authors: Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, Venugopal VasudevanAbstract:We report a series of user studies that evaluate the feasibility and usability of light-weight user authentication with a single tri-axis accelerometer. We base our investigation on uWave, a state-of-the-art recognition system for user-created free-space manipulation, or gestures. Our user studies address two types of user authentication: non-critical authentication (or identification) for a user to retrieve privacy-insensitive data; and critical authentication for protecting privacy-sensitive data. For non-critical authentication, our evaluation shows that uWave achieves high recognition accuracy (98%) and its usability is comparable with text ID-based authentication. Our results also highlight the importance of constraints for users to select their gestures. For critical authentication, the evaluation shows uWave achieves state-of-the-art resilience to attacks with 3% false positives and 3% false negatives, or 3% equal error rate. We also show that the equal error rate increases to 10% if the attackers see the users performing their gestures. This shows the limitation of gesture-based authentication and highlights the need for visual concealment.
Alessandra Lumini - One of the best experts on this subject based on the ideXlab platform.
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Combining local, regional and global matchers for a template protected on-line signature verification system
Expert Systems with Applications, 2010Co-Authors: Loris Nanni, Emanuele Maiorana, Alessandra Lumini, Patrizio CampisiAbstract:In this work an on-line signature authentication system based on an ensemble of local, regional, and global matchers is presented. Specifically, the following matching approaches are taken into account: the fusion of two local methods employing Dynamic Time Warping, a Hidden Markov Model based approach where each signature is described by means of its regional properties, and a Linear Programming Descriptor classifier trained by global features. Moreover, a template protection scheme employing the BioHashing and the BioConvolving approaches, two well known template protection techniques for biometric recognition, is discussed. The reported experimental results, evaluated on the public MCYT signature database, show that our best ensemble obtains an impressive equal error rate of 3%, when only five genuine signatures are acquired for each user during enrollment. Moreover, when the proposed protected system is taken into account, the equal error rate achieved in the worst case scenario, that is,when an ''impostor'' is able to steal the hash keys, is equal to 4.51%, whereas an equal error rate ~0 can be obtained when nobody steals the hash keys.
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A novel local on-line signature verification system
Pattern Recognition Letters, 2008Co-Authors: Loris Nanni, Alessandra LuminiAbstract:In this work, an on-line signature verification system based on local information and on a one-class classifier, the Linear Programming Descriptor classifier (LPD), is presented. The information is extracted as time functions of various dynamic properties of the signatures, then the discrete 1-D wavelet transform (WT) is performed on these features. The Discrete Cosine Transform (DCT) is used to reduce the approximation coefficients vector obtained by WT to a feature vector of a given dimension. The Linear Programming Descriptor classifier is trained using the DCT coefficients. Finally, we have studied the fusion among the approach here proposed and the state-of-the-art of the regional, the local and the global approaches. The fusion outperforms all the stand-alone approaches. Results using all the 5000 signatures from the 100 subjects of the SUBCORPUS-100 MCYT Bimodal Biometric Database are presented, yielding remarkable performance improvement both with Random and Skilled Forgeries. We want to stress that our best fusion approach obtains an equal error rate of 5.2% in the Skilled Forgeries, this value is the lowest equal error rate reported in the literature for the SUBCORPUS-100 MCYT.
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A multi-matcher system based on knuckle-based features
Neural Computing and Applications, 2007Co-Authors: Loris Nanni, Alessandra LuminiAbstract:We describe a new multi-matcher biometric approach, using knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon transform and by Haar wavelet, then these features are transformed by non-linear Fisher transform. Finally, the matching process is based on Parzen window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (EER) (near zero equal error rate).
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An improved BioHashing for human authentication
Pattern Recognition, 2007Co-Authors: Alessandra Lumini, Loris NanniAbstract:Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user specific biometric feature (BioHashing) has received much attention. These methods have significant functional advantages over sole biometrics i.e. zero equal error rate. The main drawback of the base BioHashing method proposed in the literature relies in exhibiting low performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we introduce some ideas to improve the base BioHashing approach in order to maintain a very low equal error rate when nobody steals the Hash key, and to reach good performance also when an ''impostor'' steals the Hash key.
Dario Maio - One of the best experts on this subject based on the ideXlab platform.
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Letters: Multihashing, human authentication featuring biometrics data and tokenized random number: A case study FVC2004
Neurocomputing, 2005Co-Authors: Dario Maio, Loris NanniAbstract:Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user-specific biometric feature has received much attention. These methods have significant functional advantages over solely biometrics, i.e. zero equal error rate. The main drawback of the methods proposed in the literature relies in exhibiting low performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we show that a multimodal fusion, where only one biometric characteristic is combined with the pseudo-random numbers, permits to obtain a zero equal error rate when nobody steals the pseudo-random numbers, and good performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we study the fusion among the score obtained by a Face Recognizer (where the face features are combined with pseudo-random numbers) and the scores of the systems submitted to FVC2004.
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Multihashing, human authentication featuring biometrics data and tokenized random number: A case study FVC2004
Neurocomputing, 2005Co-Authors: Dario Maio, Loris NanniAbstract:Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user-specific biometric feature has received much attention. These methods have significant functional advantages over solely biometrics, i.e. zero equal error rate. The main drawback of the methods proposed in the literature relies in exhibiting low performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we show that a multimodal fusion, where only one biometric characteristic is combined with the pseudo-random numbers, permits to obtain a zero equal error rate when nobody steals the pseudo-random numbers, and good performance when an ''impostor''B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we study the fusion among the score obtained by a Face Recognizer (where the face features are combined with pseudo-random numbers) and the scores of the systems submitted to FVC2004
Jiayang Liu - One of the best experts on this subject based on the ideXlab platform.
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user evaluation of lightweight user authentication with a single tri axis accelerometer
Human-Computer Interaction with Mobile Devices and Services, 2009Co-Authors: Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, Venugopal VasudevanAbstract:We report a series of user studies that evaluate the feasibility and usability of light-weight user authentication with a single tri-axis accelerometer. We base our investigation on uWave, a state-of-the-art recognition system for user-created free-space manipulation, or gestures. Our user studies address two types of user authentication: non-critical authentication (or identification) for a user to retrieve privacy-insensitive data; and critical authentication for protecting privacy-sensitive data. For non-critical authentication, our evaluation shows that uWave achieves high recognition accuracy (98%) and its usability is comparable with text ID-based authentication. Our results also highlight the importance of constraints for users to select their gestures. For critical authentication, the evaluation shows uWave achieves state-of-the-art resilience to attacks with 3% false positives and 3% false negatives, or 3% equal error rate. We also show that the equal error rate increases to 10% if the attackers see the users performing their gestures. This shows the limitation of gesture-based authentication and highlights the need for visual concealment.
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Mobile HCI - User evaluation of lightweight user authentication with a single tri-axis accelerometer
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services - MobileHCI '09, 2009Co-Authors: Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, Venugopal VasudevanAbstract:We report a series of user studies that evaluate the feasibility and usability of light-weight user authentication with a single tri-axis accelerometer. We base our investigation on uWave, a state-of-the-art recognition system for user-created free-space manipulation, or gestures. Our user studies address two types of user authentication: non-critical authentication (or identification) for a user to retrieve privacy-insensitive data; and critical authentication for protecting privacy-sensitive data. For non-critical authentication, our evaluation shows that uWave achieves high recognition accuracy (98%) and its usability is comparable with text ID-based authentication. Our results also highlight the importance of constraints for users to select their gestures. For critical authentication, the evaluation shows uWave achieves state-of-the-art resilience to attacks with 3% false positives and 3% false negatives, or 3% equal error rate. We also show that the equal error rate increases to 10% if the attackers see the users performing their gestures. This shows the limitation of gesture-based authentication and highlights the need for visual concealment.
