BiolD: a multimodal biometric identification system

Frischholz, Robert; Dieckmann, U.

doi:10.1109/2.820041

Cited by 297 publications

(126 citation statements)

References 5 publications

(3 reference statements)

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“…We can deÿne a multimodal biometric system as one using more than one di erent biometric characteristics to identify a person. For example, voice and face recognition can be combined to form a multimodal biometric system [24]. It is probably necessary to extend this deÿnition by including also those biometric systems that use several methods based upon the same biometric source.…”

Section: Multimodal Bissmentioning

confidence: 99%

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%

Biometric identification systems

et al. 2003

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“…First of all, the question arises of the chief purpose a multimodal biometric system is to be used for, how it operates, biometric features to be integrated, as well as how many biometric features are needed at all [6], [8]. The strength of a multimodal system relies exclusively on the characteristics of individual biometric features to be included into the system itself [3].…”

Section: Fingerprintmentioning

confidence: 99%

The Concept of Complex Events as Suitable Means for Biometric System Implementation

Rabuzin

Bača

Schatten

2007

2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI'07)

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“…Our first example is furnished by images in the BioID face databases [22]. For a subset of 27 images from the data-bases we have extracted 20 feature points.…”

Section: Real World Experimentsmentioning

confidence: 99%

A Spectral Approach to Learning Structural Variations in Graphs

Luo

Wilson

Hancock

2003

Lecture Notes in Computer Science

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This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order. Using the correspondences order, we convert the adjacency matrices to long-vectors and compute the long-vector covariance matrix. By projecting the vectorised adjacency matrices onto the leading eigenvectors of the covariance matrix, we embed the graphs in a pattern-space. We illustrate the utility of the resulting method for shape-analysis.

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BiolD: a multimodal biometric identification system

Cited by 297 publications

References 5 publications

Biometric identification systems

Biometric identification systems

The Concept of Complex Events as Suitable Means for Biometric System Implementation

A Spectral Approach to Learning Structural Variations in Graphs

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