Ageing Adaptation for Multimodal Biometrics using Adaptive Feature Set Update Algorithm

Kekre, H. B.; Bharadi, Vinayak Ashok

doi:10.1109/iadcc.2009.4809068

Cited by 14 publications

(12 citation statements)

References 7 publications

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“…However, this problem is solved by letting the user train the classifier with a reasonable amount of trials at the beginning (5 training trials gives an EER of 14.81%, as shown in the last row of Table 2) and use every subsequent authentication attempt that results in a successful login as an extra training trial. After having a robust set of training trials, an aging factor, as considered in [18], can also be introduced in which the oldest trials are weighted less and every new successful authentication attempt is taken as a new training trial. Thus the system would be adapting to the constant changes of the users and their environment.…”

Section: Implications and Discussionmentioning

confidence: 99%

Exploring Touch-Screen Biometrics for User Identification on Smart Phones

Angulo

Wästlund

2012

IFIP Advances in Information and Communication Technology

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Abstract. The use of mobile smart devices for storing sensitive information and accessing online services is increasing. At the same time, methods for authenticating users into their devices and online services that are not only secure, but also privacy and user-friendly are needed. In this paper, we present our initial explorations of the use of lock pattern dynamics as a secure and user-friendly two-factor authentication method. We developed an application for the Android mobile platform to collect data on the way individuals draw lock patterns on a touchscreen. Using a Random Forest machine learning classifier this method achieves an average Equal Error Rate (EER) of approximately 10.39%, meaning that lock patterns biometrics can be used for identifying users towards their device, but could also pose a threat to privacy if the users' biometric information is handled outside their control.

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Section: Implications and Discussionmentioning

confidence: 99%

Exploring Touch-Screen Biometrics for User Identification on Smart Phones

Angulo

Wästlund

2012

IFIP Advances in Information and Communication Technology

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“…The only condition for taking part in the evaluation was age (over 16 years old as it was demonstrated that the handwritten signature is not stable in children [23]). There were 54 right-handed and 2 left-handed.…”

Section: Evaluation Crewmentioning

confidence: 99%

Automatic usability and stress analysis in mobile biometrics

Blanco-Gonzalo¹,

Sánchez-Reillo²,

Miguel-Hurtado³

et al. 2014

Image and Vision Computing

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“…Otherwise, the system will show attenuating fusion. We are motivated by [3,4] to develop a bimodal biometric system that is more robust to environmental and sensor noise. Figure 1 shows the overall block diagram of the score level fusion strategy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient PSO Optimized Integration Weight Estimation Using D-prime Statistics for A Multibiometric System

Anzar¹

2012

IJBB

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The efficiency of a multibiometric system can be improved by weighting the scores obtained from the degraded modalities in an appropriate manner. In this paper, we propose an efficient PSO (Particle Swarm Optimization) based integration weight optimization scheme using d-prime statistics to determine the optimal weight factors for the complementary modalities, under different noise conditions. Instead of treating the weight estimation process from an algebraic point of view, an attempt is made to consider the same from the principles of linear programming techniques. The performance of the proposed method is analysed in the context of fingerprint and voice biometrics using sum rule of fusion. The d-prime statistics of fingerprint and voice modalities are measured and the integration weights are computed as the ratio of these two statistics. The computed d-prime ratio is then optimized against the recognition accuracy. The optimizing parameter is estimated in the training/validation phase using Leave-One-Out Cross Validation (LOOCV) technique. Experimental studies show that the proposed method improves recognition rate under normal operating conditions and reduces the FAR (False Acceptance Rate) considerably even at extreme low SNR (Signal to Noise Ratio) conditions. The proposed biometric solution can be easily integrated into any multibiometric system with score level fusion. Moreover, it finds extremely useful in applications where there are less number of available training samples.

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Ageing Adaptation for Multimodal Biometrics using Adaptive Feature Set Update Algorithm

Cited by 14 publications

References 7 publications

Exploring Touch-Screen Biometrics for User Identification on Smart Phones

Exploring Touch-Screen Biometrics for User Identification on Smart Phones

Automatic usability and stress analysis in mobile biometrics

An Efficient PSO Optimized Integration Weight Estimation Using D-prime Statistics for A Multibiometric System

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