Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43989
Título: Using a discrete Hidden Markov Model Kernel for lip-based biometric identification
Autores/as: Travieso, Carlos M. 
Zhang, Jianguo
Miller, Paul
Alonso, Jesús B. 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Discrete Hidden Markov Model Kernel, Image processing, Lip-based biometrics, Pattern recognition
Fecha de publicación: 2014
Editor/a: 0262-8856
Publicación seriada: Image and Vision Computing 
Resumen: In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.
URI: http://hdl.handle.net/10553/43989
ISSN: 0262-8856
DOI: 10.1016/j.imavis.2014.10.001
Fuente: Image and Vision Computing[ISSN 0262-8856],v. 32, p. 1080-1089
Colección:Artículos
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