Robust identification of persons by lips contour using shape transformation

Authors:
Juan C. Briceño;Carlos M. Travieso;Jesús B. Alonso;Miguel A. Ferrer
Affiliations:
Computer Science Department, University of Costa Rica, San Jose, Costa Rica;Signals and Communications Department, University of Las Palmas de Gran Canaria, Las Palmas de G.C., Spain;Signals and Communications Department, University of Las Palmas de Gran Canaria, Las Palmas de G.C., Spain;Signals and Communications Department, University of Las Palmas de Gran Canaria, Las Palmas de G.C., Spain
Venue:
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
Year:
2010

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Abstract

In this paper we present a biometric approach, based on lip shape. We have performed an image preprocessing, in order to detect the face of a person image. After this, we have enhanced the lips image using a color transformation, and next we do its detection. The parameterization is based on lips contour points. Those points have been transformed by a Hidden Markov Model (HMM) kernel, using a minimization of Fisher Score. Finally, a one-versus-all multiclass supervised approach based on Support Vector Machines (SVM) is applied as a classifier. A database with 50 users and 10 samples per class has been built. A cross-validation strategy have been applied in our experiments, reaching success rates up to 99.6%, using four lip training samples per class, and evaluating with six lip test samples. This success was found using a shape of 150 points, with 40 states in Hidden Markov Model and a RBF kernel for a supervised approach based on Support Vector Machines.