Information fusion in biometrics

Ross, Arun; Jain, Anil K.

doi:10.1016/s0167-8655(03)00079-5

Cited by 1,301 publications

(677 citation statements)

References 17 publications

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“…There are many and very heterogeneous ways to combine information in multibiometrics, and several taxonomies have been proposed [61,62]. The latter is very suitable to provide an overview on multibiometric systems, and states four di erent ways for combining information:…”

Section: Multimodal Bissmentioning

confidence: 99%

“…The combination of three biometric modalities (face, ÿngerprint and hand geometry) studied by Ross et al [61] illustrates an opinion fusion approach to the problem of information fusion in biometrics. Given three matching scores obtained from the three di erent biometric systems mentioned above, the problem is to develop a fusion scheme to reach an overall decision.…”

Section: Multimodal Bissmentioning

confidence: 99%

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Biometric identification systems

et al. 2003

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In this paper, we provide an overview of the fundamentals of biometric identiÿcation, together with a description of the main biometric technologies currently in use, all of them within a common reference framework. A comparison on di erent qualitative parameters of these technologies is also given, so that the reader may have a clear perspective of advantages and disadvantages of each. A section on multibiometrics describes the state of the art in making these systems work coordinately. Fusion at di erent conceptual levels is described. Finally, a section on commercial issues provides the reader a perspective of the main companies currently involved in this ÿeld. ?

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Section: Multimodal Bissmentioning

confidence: 99%

Section: Multimodal Bissmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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“…The method of sum-rule based fusion is stated in Equation (1). This procedure can be found in [2]. and are the matching scores produced by the face and fingerprint recognition, respectively.…”

Section: Proposed Multimodal Biometric Systemmentioning

confidence: 99%

“…The evidence presented by this multiple sources will be integrated in order to reduce the limitations that have been addressed by deploying single biometric systems. According to Ross and Jain [2], biometric system has four important components, which are sensor module, feature extraction module, matching module and decision-making module. A biometric data acquired from a user by a biometric sensor (e.g., a fingerprint image) is fed into the feature extraction module.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Face and Fingerprint for Robust Personal Verification System

Hassan¹,

Ramli²,

Suandi³

2014

IJMLC

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Abstract-Personal verification system that uses a single biometric trait often faces numerous limitations such as noisy sensor data, non-universality, non-distinctiveness and spoof attack. These limitations can be overcome by multimodal biometric systems that consolidate the evidence presented by multiple biometric sources and typically has better recognition performance compared to systems based on a single biometric modality. This study proposes fusion of face and fingerprint for robust recognition system. The integration is performed at the matching score level. The matching tasks for both modalities are carried out by using support vector machines (SVM) as the classifier. Experiments on face expression and fingerprint database show that the performances of multimodal biometric system provide better recognition compared to single biometric modality. Based on the fusion techniques evaluated, trait-specific weight was found to be highly effective than the sum rule-based fusion. Equal error rate (EER) percentage for face-only and fingerprint-only systems are 2.50% and 5.56%, respectively, while the EER for system using sum rule-based fusion and system using trait-specific weights are 0.833% and 0.340%, respectively.Index Terms-Multi-modal, sum-rule and trait-specific, face and fingerprint biometrics.

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“…This evolution of emotions may in fact be reflected in the brain systems that generate them, with emotions linked to survival arising from evolutionarily old brain systems. Thus the more primitive emotions would be expected to be elicited by more primitive aspect of the environment, and only at higher levels of evolution would complex classification of stimuli have had related emotions associated with them [1]. Fig.…”

Section: Stimulus For Emotionsmentioning

confidence: 99%

Galvanic Skin Response: A Physiological Sensor System for Affective Computing

Vijaya¹,

Shivakumar²

2013

IJMLC

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Abstract-Electro-dermal response of any bio-medical system is the change in electrical properties of skin due to variation in physiological and psychological conditions. The change is caused by the degree to which a person's sweat glands are active. Psychological status of a person tends to make the glands active and this change the skin resistance. Drier the skin is, higher will be the skin resistance. This variation in skin resistance ranges from 5kΩ to 25kΩ. In the current work, a subject whose Galvanic skin response (GSR) is to be measured, has been shown/played a movie clipping, images or recorded audio signals. Depending upon the theme, emotion will be evoked in the subject. Due to the change in emotion, GSR varies. This variation in GSR is recorded for a fixed time interval. In the present work, a total of 75 subjects are selected. People from different age groups and social background, both male and female have been carefully considered. Data acquisition and analysis is carried out by using LabVIEW. The results obtained are convincing and follows the ground reality with a high level of accuracy. As the subject also gives the feedback about the emotions he/she experienced, the results obtained are validated.

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Information fusion in biometrics

Cited by 1,301 publications

References 17 publications

Biometric identification systems

Biometric identification systems

Fusion of Face and Fingerprint for Robust Personal Verification System

Galvanic Skin Response: A Physiological Sensor System for Affective Computing

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