HapticV1, PascalFrancis, Corpus, bibRecord, 000191

Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

Identifieur interne : 000191 ( PascalFrancis/Corpus ); précédent : 000190; suivant : 000192

Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

Auteurs : Fawaz A. Alsulaiman ; Nizar Sakr ; Julio J. Valdes ; Abdulmotaleb El Saddik

Source :

ACM transactions on multimedia computing communications and applications [ 1551-6857 ] ; 2013.

RBID : Pascal:13-0216963

Descripteurs français

Pascal (Inist)
- Signature électronique, Caractère manuscrit, Plus proche voisin, Apprentissage probabilités, Pertinence, Type donnée, Biométrie, Sensibilité tactile, Raisonnement basé sur cas, Apprentissage supervisé, Analyse n dimensionnelle, Cryptage basé identité, Algorithme génétique, Classification à vaste marge, Estimation Bayes, Extraction forme, Modélisation, Fonction analytique, ., Forêt aléatoire, Contrôle accès.

English descriptors

KwdEn :
- Access control, Analytical function, Bayes estimation, Biometrics, Case based reasoning, Data type, Digital signature, Genetic algorithm, Identity based encryption, Manuscript character, Modeling, Multidimensional analysis, Nearest neighbour, Pattern extraction, Probability learning, Random decision forests, Relevance, Supervised learning, Tactile sensitivity, Vector support machine.

Abstract

In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 13-0216963 INIST
ET :	Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming
AU :	ALSULAIMAN (Fawaz A.); SAKR (Nizar); VALDES (Julio J.); EL SADDIK (Abdulmotaleb)
AF :	University of Ottawa/Canada (1 aut., 2 aut., 4 aut.); National Research Council Canada, Institute for Information Technology/Canada (3 aut.)
DT :	Publication en série; Niveau analytique
SO :	ACM transactions on multimedia computing communications and applications; ISSN 1551-6857; Etats-Unis; Da. 2013; Vol. 9; No. 2; 11.1-11.21; Bibl. 2 p.
LA :	Anglais
EA :	In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).
CC :	001D02B07C; 001D02B04; 001D02C03; 001D02B07B
FD :	Signature électronique; Caractère manuscrit; Plus proche voisin; Apprentissage probabilités; Pertinence; Type donnée; Biométrie; Sensibilité tactile; Raisonnement basé sur cas; Apprentissage supervisé; Analyse n dimensionnelle; Cryptage basé identité; Algorithme génétique; Classification à vaste marge; Estimation Bayes; Extraction forme; Modélisation; Fonction analytique; .; Forêt aléatoire; Contrôle accès
ED :	Digital signature; Manuscript character; Nearest neighbour; Probability learning; Relevance; Data type; Biometrics; Tactile sensitivity; Case based reasoning; Supervised learning; Multidimensional analysis; Identity based encryption; Genetic algorithm; Vector support machine; Bayes estimation; Pattern extraction; Modeling; Analytical function; Random decision forests; Access control
SD :	Firma numérica; Carácter manuscrito; Vecino más cercano; Aprendizaje probabilidades; Pertinencia; Tipo dato; Biometría; Sensibilidad tactil; Razonamiento fundado sobre caso; Aprendizaje supervisado; Análisis n dimensional; Cifrado fundado sobre identidad; Algoritmo genético; Máquina ejemplo soporte; Estimación Bayes; Extracción forma; Modelización; Función analítica; Bosque aleatorio; Control de Acceso
LO :	INIST-28207.354000504148130030
ID :	13-0216963

Links to Exploration step

Pascal:13-0216963

Le document en format XML

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<s5>10</s5>
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<fC03 i1="05" i2="X" l="ENG"><s0>Relevance</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Pertinencia</s0>
<s5>10</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Type donnée</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Data type</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Tipo dato</s0>
<s5>11</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Biométrie</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Biometrics</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Biometría</s0>
<s5>12</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Sensibilité tactile</s0>
<s5>18</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Tactile sensitivity</s0>
<s5>18</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Sensibilidad tactil</s0>
<s5>18</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Raisonnement basé sur cas</s0>
<s5>19</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Case based reasoning</s0>
<s5>19</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Razonamiento fundado sobre caso</s0>
<s5>19</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Apprentissage supervisé</s0>
<s5>20</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Supervised learning</s0>
<s5>20</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Aprendizaje supervisado</s0>
<s5>20</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Analyse n dimensionnelle</s0>
<s5>21</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Multidimensional analysis</s0>
<s5>21</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Análisis n dimensional</s0>
<s5>21</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Cryptage basé identité</s0>
<s5>23</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Identity based encryption</s0>
<s5>23</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Cifrado fundado sobre identidad</s0>
<s5>23</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Algorithme génétique</s0>
<s5>24</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Genetic algorithm</s0>
<s5>24</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Algoritmo genético</s0>
<s5>24</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Classification à vaste marge</s0>
<s5>25</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Vector support machine</s0>
<s5>25</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Máquina ejemplo soporte</s0>
<s5>25</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Estimation Bayes</s0>
<s5>26</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Bayes estimation</s0>
<s5>26</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Estimación Bayes</s0>
<s5>26</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Extraction forme</s0>
<s5>27</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Pattern extraction</s0>
<s5>27</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Extracción forma</s0>
<s5>27</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>28</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Modeling</s0>
<s5>28</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Modelización</s0>
<s5>28</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Fonction analytique</s0>
<s5>29</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Analytical function</s0>
<s5>29</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Función analítica</s0>
<s5>29</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>.</s0>
<s4>INC</s4>
<s5>82</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE"><s0>Forêt aléatoire</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG"><s0>Random decision forests</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA"><s0>Bosque aleatorio</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE"><s0>Contrôle accès</s0>
<s4>CD</s4>
<s5>97</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG"><s0>Access control</s0>
<s4>CD</s4>
<s5>97</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA"><s0>Control de Acceso</s0>
<s4>CD</s4>
<s5>97</s5>
</fC03>
<fN21><s1>203</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 13-0216963 INIST</NO>
<ET>Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming</ET>
<AU>ALSULAIMAN (Fawaz A.); SAKR (Nizar); VALDES (Julio J.); EL SADDIK (Abdulmotaleb)</AU>
<AF>University of Ottawa/Canada (1 aut., 2 aut., 4 aut.); National Research Council Canada, Institute for Information Technology/Canada (3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>ACM transactions on multimedia computing communications and applications; ISSN 1551-6857; Etats-Unis; Da. 2013; Vol. 9; No. 2; 11.1-11.21; Bibl. 2 p.</SO>
<LA>Anglais</LA>
<EA>In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).</EA>
<CC>001D02B07C; 001D02B04; 001D02C03; 001D02B07B</CC>
<FD>Signature électronique; Caractère manuscrit; Plus proche voisin; Apprentissage probabilités; Pertinence; Type donnée; Biométrie; Sensibilité tactile; Raisonnement basé sur cas; Apprentissage supervisé; Analyse n dimensionnelle; Cryptage basé identité; Algorithme génétique; Classification à vaste marge; Estimation Bayes; Extraction forme; Modélisation; Fonction analytique; .; Forêt aléatoire; Contrôle accès</FD>
<ED>Digital signature; Manuscript character; Nearest neighbour; Probability learning; Relevance; Data type; Biometrics; Tactile sensitivity; Case based reasoning; Supervised learning; Multidimensional analysis; Identity based encryption; Genetic algorithm; Vector support machine; Bayes estimation; Pattern extraction; Modeling; Analytical function; Random decision forests; Access control</ED>
<SD>Firma numérica; Carácter manuscrito; Vecino más cercano; Aprendizaje probabilidades; Pertinencia; Tipo dato; Biometría; Sensibilidad tactil; Razonamiento fundado sobre caso; Aprendizaje supervisado; Análisis n dimensional; Cifrado fundado sobre identidad; Algoritmo genético; Máquina ejemplo soporte; Estimación Bayes; Extracción forma; Modelización; Función analítica; Bosque aleatorio; Control de Acceso</SD>
<LO>INIST-28207.354000504148130030</LO>
<ID>13-0216963</ID>
</server>
</inist>
</record>

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Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

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