Using a discrete Hidden Markov Model Kernel for lip-based biometric identification

Carlos M. Travieso (Lead / Corresponding author), Jianguo Zhang (Lead / Corresponding author), Paul Miller, Jesús B. Alonso

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1080-1089
Number of pages10
JournalImage and Vision Computing
Volume32
Issue number12
Early online date22 Oct 2014
DOIs
Publication statusPublished - Dec 2014

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Keywords

  • Discrete Hidden Markov Model Kernel
  • Image processing
  • Lip-based biometrics
  • Pattern recognition

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