Your browser does not support JavaScript!

Score bi-Gaussian equalisation for multimodal person verification

Score bi-Gaussian equalisation for multimodal person verification

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Multimodal biometric fusion at score level can be performed by means of combinatory or classificatory techniques. In the first case, it is straightforward that the normalisation of the scores is a very important issue for the success of the fusion process. In the classificatory approach as, for instance, in support vector machine (SVM)-based systems, only simple normalisation methods are usually applied. In this work, histogram equalisation of biometric score distribution is successfully applied in a multimodal person verification system composed by prosodic, speech spectrum and face information. Furthermore, a new bi-Gaussian equalisation (BGEQ) is introduced, which takes into account the separate statistics of the genuine and impostor scores by using as a reference a sum of two Gaussian functions, whose standard deviations model the overlap between the genuine and impostor lobes of the original distributions. Multimodal verification experiments are shown, where prosodic and speech spectrum scores are provided by speech experts using the Switchboard-I database, and face scores are obtained by a face recognition expert using XM2VTS database. BGEQ in combination with an SVM fusion system with a polynomial kernel has obtained the best results and has outperformed in more than a 21.29% the results obtained by min–max normalisation.

Related content

This is a required field
Please enter a valid email address