Face recognition methods for multimodal interface

In this paper we provide a comparative study of several conventional face recognition methods (PCA, KPCA, GDA, SVM and RBF) that are suitable to work properly in multimodal systems. Performance of these systems is often influenced by various negative effects of the real-world environment. We evaluat...

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Bibliographic Details
Main Authors: Ban, J., Pavlovicova, J., Feder, M., Omelina, L., Oravec, M.
Format: Conference Proceeding
Language:English
Subjects:
Face
Face recognition
Kernel
Lighting
machine learning methods
multimodal interface
Principal component analysis
Support vector machines
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Summary:In this paper we provide a comparative study of several conventional face recognition methods (PCA, KPCA, GDA, SVM and RBF) that are suitable to work properly in multimodal systems. Performance of these systems is often influenced by various negative effects of the real-world environment. We evaluate the influence of varying illuminations and pose of faces on face recognition accuracy. Based on the results of our experiments we select the most suitable face recognition methods for application in HBB-NEXT project. This project wants to lay the foundations for advanced hybrid multiuser services.
DOI:10.1109/WMNC.2012.6416164