Multimodal Biometric Person Authentication Using Fingerprint, Face Features

Long, Tran Binh; Thai, Le Hoang; Hanh, Tran

doi:10.1007/978-3-642-32695-0_54

Cited by 20 publications

(13 citation statements)

References 12 publications

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“…To overcome these difficulties multi-biometric systems are used [11], [42], [25]. Many of these limitations can be addressed by deploying multi-modal biometric systems that integrate the evidences presented by multiple sources of information [16], [27], [42], [44], [49]. This paper is arranged in 5 sections.…”

Section: Introductionsupporting

confidence: 42%

An Improved Intelligent Multi Biometric Authentication System

Mercy¹,

E.O.²

2015

CAE

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The beginning of the 21st century was marked with events that focused on the world's attention to public security. Increase in technological advancement gave people possibilities of information transfer and ease of physical mobility unseen before. With those possibilities comes risk of fraud, theft of personal data, or even theft of identity. One of the ways to prevent this is through biometric authentication system. Unibiometric systems rely on the evidence of a single source of information whereas multibiometric systems consolidate multiple sources of biometric evidences. Multibiometric systems, if designed properly, are able to enhance the matching performance. In this paper, Intelligent Multi-Biometric Authentication System, face and fingerprint biometric traits are used. When the images are captured, preprocessing in face and fingerprint images is done using Enhanced Extracted Face (EEF) method and Plainarized Region Of Interest (PROI) method respectively. These are fed into a Cascaded Link Feed Forward Neural Network(CLFFNN) which is a classifier trained with backpropagation algorithm. CLFFNN comprises of CLFFNN(1) used for training and CLFFNN(2) used as the main classifier. They are arranged in cascades. Afterwards, both outputs from face and fingerprints are combined using AND operation.

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Section: Introductionsupporting

confidence: 42%

An Improved Intelligent Multi Biometric Authentication System

Mercy¹,

E.O.²

2015

CAE

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“…To overcome these difficulties multi-biometric systems are used [10], [41], [24]. Many of these limitations can be addressed by deploying multi-modal biometric systems that integrate the evidences presented by multiple sources of information [15], [26], [41], [43], [48]. This paper is arranged in 5 sections.…”

Section: Introductionsupporting

confidence: 42%

An Efficient Image Preprocessing in an Improved Intelligent Multi Biometric Authentication System

Mercy¹,

E.O.²

2015

IJAIS

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The quality of the biometric feature obtained after biometric image extraction and preprocessing improves classifier accuracy and determines the degree and standard of user authentication, to a large extent. Preprocessing is the process of preparing the input images (face or fingerprints) to be ready for the next step of the authentication system, in order to produce a good enough quality of output face or fingerprint image. In this paper, we present an efficient face and finger print image preprocessing using Enhanced Extracted Face (EEF) method and Plainarized Region of Interest (PROI) method respectively. The aim is to reduce one or more of the following -False accept rate (FAR), False reject rate (FRR), Failure to enroll rate (FTE) and increase accuracy and recognition speed.

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“…In one recent research in [21], a multimodal biometric system using face and fingerprint features with the incorporation of Zernike Moment (ZM) and Radial Basis Function (RBF) Neural Network for personal authentication is reported. It has been proven that face authentication is fast but not reliable while fingerprint authentication is reliable but inefficient in database retrieval, considering which our proposed system has been developed in such a way that it can avoid the advantages of those uni-modal biometric systems and acquire the variations in an individual's image of face or fingerprint.…”

Section: Previous Workmentioning

confidence: 46%

Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint

Tran

Le²

2015

IJMECS

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Abstract-Multimodal biometric systems have proven more efficient in personal verification or identification than single biometric ones, so it is also a focus of this paper. Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is done using Relevance Vector Machine (RVM) and feature-level fusion technique. The proposed system has proven its remarkable ability to overcome the limitations of unimodal biometric systems and to tolerate local variations in the face or fingerprint image of an individual. Also, the achieved experimental results have demonstrated that using RVM can assure a higher level of forge resistance and enables faster authentication than the state-of-the-art technique , namely the support vector machine (SVM).

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Multimodal Biometric Person Authentication Using Fingerprint, Face Features

Cited by 20 publications

References 12 publications

An Improved Intelligent Multi Biometric Authentication System

An Improved Intelligent Multi Biometric Authentication System

An Efficient Image Preprocessing in an Improved Intelligent Multi Biometric Authentication System

Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint

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