Difference between revisions of "Publications:Lip Biometrics for Digit Recognition"

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(Created page with "<div style='display: none'> == Do not edit this section == </div> {{PublicationSetupTemplate|Author=Maycel I. Faraj, Josef Bigun |PID=239344 |Name=Faraj, Maycel I. [mafa] (Hö...")
 
 
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|PID=239344
 
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|Name=Faraj, Maycel I. [mafa] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Bigun, Josef [josef] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938])
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|Name=Faraj, Maycel I. (mafa) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Bigun, Josef (josef) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938))
 
|Title=Lip Biometrics for Digit Recognition
 
|Title=Lip Biometrics for Digit Recognition
 
|PublicationType=Conference Paper
 
|PublicationType=Conference Paper

Latest revision as of 21:41, 30 September 2016

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Title Lip Biometrics for Digit Recognition
Author Maycel I. Faraj and Josef Bigun
Year 2007
PublicationType Conference Paper
Journal
HostPublication Computer Analysis of Images and Patterns, Proceedings
Conference 12th International Conference on Computer Analysis of Images and Patterns, Vienna, AUSTRIA, AUG 27-29, 2007
DOI http://dx.doi.org/10.1007/978-3-540-74272-2_45
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239344
Abstract This paper presents a speaker-independent audio-visual digit recognition system that utilizes speech and visual lip signals. The extracted visual features are based on line-motion estimation obtained from video sequences with low resolution (128 ×128 pixels) to increase the robustness of audio recognition. The core experiments investigate lip motion biometrics as stand-alone as well as merged modality in speech recognition system. It uses Support Vector Machines, showing favourable experimental results with digit recognition featuring 83% to 100% on the XM2VTS database depending on the amount of available visual information.