Local Binary Pattern Based Features for Sign Language Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F11%3A43898225" target="_blank" >RIV/49777513:23520/11:43898225 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1134/S1054661811020416" target="_blank" >http://dx.doi.org/10.1134/S1054661811020416</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1134/S1054661811020416" target="_blank" >10.1134/S1054661811020416</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Local Binary Pattern Based Features for Sign Language Recognition

Popis výsledku v původním jazyce

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. Sincethe 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.

Název v anglickém jazyce

Local Binary Pattern Based Features for Sign Language Recognition

Popis výsledku anglicky

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. Sincethe 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/ME08106" target="_blank" >ME08106: Vývoj integrálního multimodálního pomocného systému</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Pattern Recognition and Image Analysis

ISSN

1054-6618

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

RU - Ruská federace

Počet stran výsledku

4

Strana od-do

398-401

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—