Fusion of face and speech data for person identity verification

Ben-Yacoub, Souheil; Abdeljaoued, Yousri; Mayoraz, Eddy

doi:10.1109/72.788647

Cited by 280 publications

(151 citation statements)

References 23 publications

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“…Results clearly favor the Bayesian scheme with respect to the mean scheme; however, no other comparisons are carried out with, for instance, the previous approach. Other examples of opinion fusion strategies and complex multibiometric schemes can be seen in [76,65,41,69].…”

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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“…There is some work on fusing different biometric modalities (e.g. face and speech [3], face and fingerprint [23]), but most studies concentrate on fusing different representations of a single underlying modality (e.g. 2D and 3D facial shape in [6]).…”

Section: Related Workmentioning

confidence: 99%

Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

Tan

Triggs

Analysis and Modeling of Faces and Gestures

229

139

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Abstract. Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results.

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“…Examples are global-learning-global-decision (GG) (Brunelli and Falavigna, 1995;Bigun et al, 1997;Kittler et al, 1998;Hong and Jain, 1998;Ben-Yacoub et al, 1999;Chatzis et al, 1999;Verlinde et al, 2000), local-learning-globaldecision (LG) (Jain and Ross, 2002;Kumar and Zhang, 2003;Indovina et al, 2003;Fierrez-Aguilar et al, 2004;Wang et al, 2004;Toh et al, 2004;Poh and Bengio, 2005), and similarly global-learninglocal-decision (GL) (Jain and Ross, 2002;Toh et al, 2004), and local-learninglocal-decision (LL) (Toh et al, 2004). In the present work we adhere to this taxonomy and extend it by incorporating new items: adapted-learning and adapted-decisions.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…A common practice in most of the reported works on multimodal biometrics is to combine the matching scores obtained from the unimodal systems by using simple rules (e.g., sum, product), statistical methods, or machine learning procedures (Brunelli and Falavigna, 1995;Bigun et al, 1997;Kittler et al, 1998;Hong and Jain, 1998;Ben-Yacoub et al, 1999;Chatzis et al, 1999;Verlinde et al, 2000). A remarkable characteristic of this approach, as compared to the feature-level combination techniques, is the possibility of designing structured multimodal systems by using existing unimodal recognition strategies (Maltoni et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Adapted user-dependent multimodal biometric authentication exploiting general information

Fiérrez-Aguilar

Garcia‐Romero

Ortega‐Garcia

et al. 2005

Pattern Recognition Letters

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This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 26.16 (2005): 2628 – 2639, DOI: 10.1016/j.patrec.2005.06.008A novel adapted strategy for combining general and user-dependent knowledge at the decision-level in multimodal biometric authentication is presented. User- independent, user-dependent, and adapted fusion and decision schemes are com- pared by using a bimodal system based on ¯ngerprint and written signature. The adapted approach is shown to outperform the other strategies considered in this pa- per. Exploiting available information for training the fusion function is also shown to be better than using existing information for post-fusion trained decisions.This work has been supported by the Spanish Ministry for Science and Tech- nology under projects TIC2003-09068-C02-01 and TIC2003-08382-C05-01

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Fusion of face and speech data for person identity verification

Cited by 280 publications

References 23 publications

Biometric identification systems

Biometric identification systems

Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

Adapted user-dependent multimodal biometric authentication exploiting general information

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