Multimodal decision-level fusion for person authentication

Chatzis, Vassilios; Borş, Adrian G.; Pitas, Ioannis

doi:10.1109/3468.798073

Cited by 124 publications

(66 citation statements)

References 13 publications

(2 reference statements)

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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%

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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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Section: Related Work and Motivationmentioning

confidence: 99%

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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show abstract

“…The recognition accuracy of this system was found to result in better recognition accuracy when compared to the corresponding data of the respective unimodal systems. Ghattis et al (1999) [5] explored the effectiveness of a biomodal system using speech and face traits. Frischholz et.al (2000) [6] developed a trimodal biometric system with face, speech and lip movement.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of unimodal, bimodal and trimodal biometric identification system

Kovoor¹

2017

IJCA

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Multimodal biometric systems that integrate the evidences presented by multiple sources of informationaddressed the limitation of the unimodal biometric systems with a variety of problems, namely, noisy data, intra-class variations, restricted degrees of freedom, non-universality and spoof attacks. The likelihood ratio based score fusion framework was developed specifically for the verification scenario where the goal is to decide whether an input sample belongs to the genuine or impostor class. Twenty users were randomly selected from the population. The various biometric traits, namely face, voice and signature of ten users were used to train the respective modules of the proposed identification system. As a general observation for three scenarios, the p-value in the ANOVA analysis conclude that the fusion of three modalities brings systematically a clear improvement of the results in comparison to either unimodal and bimodal.

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“…• Decision level fusion: the modalities are processed separately to build independent models [37]. This approach completely ignores the correlation between features and allows complete asynchrony between the streams.…”

Section: Multistream Hmmsmentioning

confidence: 99%

Ensemble learning method for hidden markov models.

Hamdi¹

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This dissertation is dedicated to the memory of my father.iii Thus, there is a need for building a classification system that takes into account the variations within each class in the data.This dissertation introduces an ensemble learning method for temporal data that uses a mixture of Hidden Markov Model (HMM) classifiers. We hypothesize that the data are generated by K models, each of which reflects a particular trend in the data. Model identification could be achieved through clustering in the feature space or in the parameters space. However, this approach is inappropriate in the context of sequential data. The proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence.This will result in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE) based discriminative, and the Variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the multiple models outputs using a decision level fusion method such as an artificial neural network or a hierarchical mixture of experts.v Our approach was evaluated on two real-world applications: (1) identification of CardioPulmonary Resuscitation (CPR) scenes in video simulating medical crises; and (2) landmine detection using Ground Penetrating Radar (GPR). Results on both applications show that the proposed method can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data.vi

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Multimodal decision-level fusion for person authentication

Cited by 124 publications

References 13 publications

Adapted user-dependent multimodal biometric authentication exploiting general information

Adapted user-dependent multimodal biometric authentication exploiting general information

Performance evaluation of unimodal, bimodal and trimodal biometric identification system

Ensemble learning method for hidden markov models.

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