Qualitative fusion of normalised scores in multimodal biometrics

Alsaade, Fawaz Waselallah; Ariyaeeinia, Aladdin M.; Malegaonkar, A.; Pillay, S.

doi:10.1016/j.patrec.2008.12.008

Cited by 13 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, in the right panel of Figure 10 , since the distance values are normalized, it allows us to use a single threshold value for all modalities so that parameter tuning procedure can be simplified. This normalization procedure is similar to score normalization [ 51 , 52 ]. Algorithm 1 Majority Voting Based Decision Level Fusion For the given threshold

, the number of classifier

and the feature vector

, the decision level majority voting accepts if

where

outputs the greatest integer that is less than or equal to

and

is an individual classifier such that

…”

Section: Methodsmentioning

confidence: 99%

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

Kim

Chun

et al. 2018

Sensors

View full text Add to dashboard Cite

Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate (EER) and 100% detection probability at 1% FAR (false acceptance rate) (PD.1), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD.1, showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.

show abstract

, the number of classifier

and the feature vector

, the decision level majority voting accepts if

where

outputs the greatest integer that is less than or equal to

and

is an individual classifier such that

…”

Section: Methodsmentioning

confidence: 99%

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

Kim

Chun

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Bendris et al introduced quality measures in audio-visual identity verification [18]. Alsaade et al showed that score normalization and quality-based fusion improve the accuracy of multimodal biometrics [2]. Optimal integration weight estimation using least squares technique was reported in [19].…”

Section: Previous Workmentioning

confidence: 99%

“…This average distance is not an adequate performance measure since a large value ofρ 2 does not imply that the classes are well separated. The performance measure well suited to express the separability of classes is the ratio between inter-class and intra-class distance [29].…”

Section: Estimation Of Inter-/intra-class Distancementioning

confidence: 99%

“…One of the main problems associated with biometric systems is the undesired variations in the biometric data. These variations can arise due to a variety of factors such as sensors used in capturing the biometric data and various non-ideal operating conditions such as background noise and non-uniform illumination [1][2][3][4][5][6][7][8][9]. Multimodal systems are more robust to environmental and sample quality variations due to the presence of multiple sources of evidences [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

Anzar

Sathidevi

2014

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics. An efficient matching score preprocessing technique based on multi-normalization is employed for improving the performance of the multimodal system, under various noise conditions. Ancillary measures derived from the feature space and the score space are used in addition to the matching score vectors, for weighing the modalities, based on their relative degradation. Reliability (dispersion) and the separability (inter-/intra-class distance and d-prime statistics) measures under various noise conditions are estimated from the individual modalities, during the training/validation stage. The 'best integration weights' are then computed by algebraically combining these measures using the weighted sum rule. The computed integration weights are then optimized against the recognition accuracy using techniques such as grid search, genetic algorithm and particle swarm optimization. The experimental results show that, the proposed biometric solution leads to considerable improvement in the recognition performance even under low signal-to-noise ratio (SNR) conditions and reduces the false acceptance rate (FAR) and false rejection rate (FRR), making the system useful for security as well as forensic applications.

show abstract

“…[5]. Alsaade et al in 2009 showed that score normalization and quality-based fusion improves the accuracy of multimodal biometrics [3]. Optimal integration weight estimation using least squares technique was reported in [23].…”

Section: Introductionmentioning

confidence: 99%

Optimal Score Level Fusion using Modalities Reliability and Separability Measures

Anzar¹,

Sathidevi²

2012

IJCA

View full text Add to dashboard Cite

This paper presents two optimal fusion techniques using reliability and separability measures for a multibiometric system, employing fingerprints and voice. In the first method, reliabilities of fingerprint and voice modalities are measured and the integration weights are computed as the ratio of these two reliabilities. The computed reliability ratio is then optimized against the recognition accuracy. The optimizing parameter is estimated in the training/validating phase. Latter is a multi-normalization based fusion scheme, where the separability measures are used as the integration weights to improve the performance of the biometric system. The inter/intra class separability measures and the d-prime separability measures, under various noise conditions are estimated in the training/validation stage. Performance of the proposed methods are compared with that of the baseline techniques using score level fusion. Experimental studies show that, the proposed methods improve global recognition rate and reduce the False Acceptance Rate (FAR) and False Rejection Rate (FRR) over the baseline systems. The advantage of the proposed biometric techniques is that they can be easily integrated into any multibiometric system with score level fusion and find extremely useful in applications especially with less number of available training samples.

show abstract

Qualitative fusion of normalised scores in multimodal biometrics

Abstract: A new approach to enhancing the accuracy of multimodal biometrics is investigated. The proposed approach, which involves combining score normalisation and qualitative-based fusion, is shown to considerably improve the accuracy of multimodal biometrics under different data conditions.

Cited by 13 publications

References 10 publications

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

Optimal Score Level Fusion using Modalities Reliability and Separability Measures

Contact Info

Product

Resources

About