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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F11%3A43898225%21RIV12-MSM-23520___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
In this paper we focus on appearance features describing the manual component of Sign Language particularly the Local Binary Patterns. We compare the performance of these features with geometric moments describing the trajectory and shape of hands. Since the non-manual component is also very important for sign recognition we localize facial landmarks via Active Shape Model combined with Landmark detector that increases the robustness of model fitting. We test the recognition performance of individual features and their combinations on a database consisting of 11 signers and 23 signs with several repetitions. Local Binary Patterns outperform the geometric moments. When the features are combined we achieve a recognition rate up to 99.75% for signer dependent tests and 57.54% for signer independent tests. In this paper we focus on appearance features describing the manual component of Sign Language particularly the Local Binary Patterns. We compare the performance of these features with geometric moments describing the trajectory and shape of hands. Since the non-manual component is also very important for sign recognition we localize facial landmarks via Active Shape Model combined with Landmark detector that increases the robustness of model fitting. We test the recognition performance of individual features and their combinations on a database consisting of 11 signers and 23 signs with several repetitions. Local Binary Patterns outperform the geometric moments. When the features are combined we achieve a recognition rate up to 99.75% for signer dependent tests and 57.54% for signer independent tests.
dcterms:title
Local Binary Pattern Based Features for Sign Language Recognition Local Binary Pattern Based Features for Sign Language Recognition
skos:prefLabel
Local Binary Pattern Based Features for Sign Language Recognition Local Binary Pattern Based Features for Sign Language Recognition
skos:notation
RIV/49777513:23520/11:43898225!RIV12-MSM-23520___
n14:predkladatel
n15:orjk%3A23520
n3:aktivita
n11:S n11:P
n3:aktivity
P(ME08106), S
n3:cisloPeriodika
3
n3:dodaniDat
n18:2012
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n9:9379991 n9:7180659 n9:3572072
n3:druhVysledku
n8:J
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
209786
n3:idVysledku
RIV/49777513:23520/11:43898225
n3:jazykVysledku
n20:eng
n3:klicovaSlova
Local Binary Pattern, Sign Language, Sign Language Recognition
n3:klicoveSlovo
n16:Sign%20Language n16:Sign%20Language%20Recognition n16:Local%20Binary%20Pattern
n3:kodStatuVydavatele
RU - Ruská federace
n3:kontrolniKodProRIV
[B2023E5FD1B7]
n3:nazevZdroje
Pattern Recognition and Image Analysis
n3:obor
n5:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n17:ME08106
n3:rokUplatneniVysledku
n18:2011
n3:svazekPeriodika
21
n3:tvurceVysledku
Železný, Miloš Trojanová, Jana Hrúz, Marek
s:issn
1054-6618
s:numberOfPages
4
n7:doi
10.1134/S1054661811020416
n19:organizacniJednotka
23520