Multimodal Biometric Systems - Study to Improve Accuracy and Performance

Sasidhar, Kalyan; Kakulapati, V.; Ramakrishna, Kolikipogu; K.KailasaRao,

doi:10.5121/ijcses.2010.1205

Cited by 27 publications

(10 citation statements)

References 14 publications

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“…In our approach the identification decision results from a post-classification biometric fusion rule, which is the most implemented fusion scheme in the actual multimodal biometric systems integrating multiple and independent human traits [12], [13]. The applied post-classification biometric fusion rule is the weighted voting scheme in which each biometric has its weight for the final identification decision.…”

Section: Biometric Fusiom Iterative Proceduresmentioning

confidence: 99%

An optimized multimodal biometric system with hierachical classifiers and reduced features

Soviany¹,

Puşcoci²

2014

2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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The paper presents an optimized multimodal biometric system for identification applications. This solution is based on innovative and computational-efficient biometric data hierarchical classifiers (with detection and discrimination stages) and also with a post-classification fusion. The detection-based classification is very suitable for applications with several security levels in which the end-users have various authorization degrees. The proposed solution supports an optimal trade-off between the identification accuracy and the computational complexity, which is important for the medium-and large-scale identification biometric applications.

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Section: Biometric Fusiom Iterative Proceduresmentioning

confidence: 99%

An optimized multimodal biometric system with hierachical classifiers and reduced features

Soviany¹,

Puşcoci²

2014

2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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“…But this system used fingerprints only for authentication and also if the fingerprint is compromised, the entire communication would be compromised. Also the uni-modal biometric systems are having many disadvantages over multimodal biometric systems and are explained by Sasidhar et al (2010). They examined the accuracy and performance of multimodal biometric authentication systems using state of the art Commercial Off-The-Shelf products.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Biometric-based Secured Authentication System using Steganography

M.¹

2012

Journal of Computer Science

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Problem statement: The main challenge in the design of a security system for high security mobile ad OC network is how to prevent the attacks against data modification and authentication. Approach: In this novel proposed system the messages communicated between the users are encrypted by the cancellable cryptographic key generated from fingerprint features of the receiver by applying genetic operators and are embedded inside the scrambled face biometrics of the sender using steganography method. Results: The receiver first unscrambles the facial image of the sender and separates the facial image and the encrypted data. Then he verifies the sender by using an Eigen face recognition algorithm and if he is a genuine sender he decrypts the cipher text with the key generated using the receiverâs fingerprint biometric. Conclusion/Recommendations: By this way, the receiver ensures the genuineness of the sender and data confidentiality. Revocability is also guaranteed since we apply genetic operator to randomize the cryptographic key whenever it is necessary. By simulation results, we also show that the proposed method is more efficient mechanism for authentication and security

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“…Authentication systems setup with one biometric modality may not be sufficient for the pertinent application in terms of properties such as universality, distinctiveness, acceptability etc. Unimodal biometric systems are lacking operational advantages pertaining to the performance and accuracy [1]. The face, fingerprint and voice recognition problem can be divided further main stages: face, fingerprint and voice detection (verification), and authentication based on the face, fingerprint and voice detected and verified.…”

Section: Introductionmentioning

confidence: 99%

Application Based Multimodal Biometric System for Speech Controlled Device

Ramesh¹

2017

IJRASET

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Multimodal biometric systems have been widely used to overcome the limitations of unimodal biometric systems and to achieve high recognition accuracy. Unimodal biometric systems developed for each of the biometric features may not always meets the required performance. We introduce a system on JAVA platform, which integrates face recognition, fingerprint verification, and speech recognition for personal identification. The methods are analysed to integrate the various features together to acquire a multimodal biometric system. This system takes advantage of the capabilities of each individual biometrics. The processed information from all these three modules is combined with the help of fusion algorithm in context switching in which feature vectors are made independently for query images and are then compared to the enrolment templates which are stored during database preparation for each biometric trait. Further, this paper is to propose the integration of Biometric technology with Automobile system and controlling the system using Speech input once the authentication is done, through an Android device and Bluetooth module for portability, thus visualizing the future possibilities of Biometric Authentication and its applications in vehicular world.

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Multimodal Biometric Systems - Study to Improve Accuracy and Performance

Abstract: ABSTRACT

Cited by 27 publications

References 14 publications

An optimized multimodal biometric system with hierachical classifiers and reduced features

An optimized multimodal biometric system with hierachical classifiers and reduced features

Multimodal Biometric-based Secured Authentication System using Steganography

Application Based Multimodal Biometric System for Speech Controlled Device

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