Unimodal and Multimodal Biometric Sensing Systems: A Review

Oloyede, Muhtahir O.; Hancke, Gerhard P.

doi:10.1109/access.2016.2614720

Cited by 121 publications

(75 citation statements)

References 169 publications

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“…The positive class is also called target class and the data from the other classes which is not available during training is called negative, or outlier class. For example, a unimodal biometric system uses a single biometric trait for verification or identification [37]. Support Vector Data Description (SVDD) [38] is among the most widely used one-class classification methods used for anomaly detection and other related applications.…”

Section: One-class Classificationmentioning

confidence: 99%

Subspace Support Vector Data Description

Sohrab

Raitoharju

Gabbouj

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. The data description in the new subspace is obtained by Support Vector Data Description. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulation. We conduct experiments on two multimodal datasets and compare the proposed approach with baseline and recently proposed one-class classification methods combined with early fusion and also considering each modality separately. We show that the proposed Multimodal Subspace Support Vector Data Description outperforms all the methods using data from a single modality and performs better or equally well than the methods fusing data from all modalities.

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Section: One-class Classificationmentioning

confidence: 99%

Subspace Support Vector Data Description

Sohrab

Raitoharju

Gabbouj

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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“…The techniques used for verifying user identity [35] can vary from simple 4 digit pin, facial recognition, retinal scan verification, fingerprint verification to sophisticated biometric gait recognition, vein recognition and voice recognition. There can be one or more Identity Verification Mechanism (IVM) depending on area secured, user preferences and security requirements.…”

Section: A Primary Access Pointmentioning

confidence: 99%

Improving Smart Home Security: Integrating Logical Sensing Into Smart Home

2017

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Abstract-The paper explains various security issues in the existing home automation systems and proposes the use of logic based security algorithms to improve home security. The work classifies natural access points to a home as primary and secondary access points depending on their use. Logic based sensing is implemented by identifying normal user behavior at these access points and requesting user verification when necessary. User position is also considered when various access points changed states. Moreover, the algorithm also verifies the legitimacy of a fire alarm by measuring the change in temperature, humidity and carbon monoxide levels, thus defending against manipulative attackers. The experiment conducted in this paper used a combination of sensors, microcontrollers, Raspberry Pi and ZigBee communication to identify user behavior at various access points and implement the logical sensing algorithm. In the experiment the proposed logical sensing algorithm was successfully implemented for a month in a studio apartment. During the course of the experiment the algorithm was able to detect all the state changes of the primary and secondary access points and also successfully verified user identity 55 times generating 14 warnings and 5 alarms.

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“…[1] A multimodal system reduces failure to enroll and resists unauthorized access, as it is difficult to spoof multiple biometric sources simultaneously. Multimodal systems can search large databases efficiently and quickly through use of simple but less accurate modality to narrow down the options in the database before applying complex and accurate modality on remaining data for final identification.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Biometric Systems: A Review

Thadeshwar

Patel

et al. 2018

ijarcs

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Abstract:The need for biometrics is rising with an increased need for security in every organization of the world. Every attribute, fingerprint, iris, knuckle print, face, palm print, is unique to an individual and can aid in recognition. Unimodal biometric systems involve the use of a single biometric attribute for identification. However, with the rise of duping, there is a requirement for constructing an efficient system using multiple biometric attributes. In multimodal systems, fusion of various biometric identities into a single vector is done using algorithms. This paper covers several different techniques used for biometric authentication. These use varying numbers of attributes. Our future project focuses on the research of different techniques of multimodal recognition in order to devise a technique that is robust and has an increased efficiency in recognition and identification.

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Unimodal and Multimodal Biometric Sensing Systems: A Review

Cited by 121 publications

References 169 publications

Subspace Support Vector Data Description

Subspace Support Vector Data Description

Improving Smart Home Security: Integrating Logical Sensing Into Smart Home

Multimodal Biometric Systems: A Review

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