The problem of high dimensionality in face verification tasks has recently been simplified by the use of underlying spatial structures as proposed in the 2DPCA, 2DLDA and CSA methods. Fusion techniques at both levels, feature extraction and matching score, have been developed to join the information obtained and achieve better results in verification process. The application of these advances to facial verification techniques using different SVM schemes as classification algorithm is here shown. The experiments have been performed over a wide facial database (FRAV2D including 109 subjects), in which only one interest variable was changed in each experiment. For training the SVMs, only two images per subject have been provided to fit in the small sample size problem.
CITATION STYLE
Bosin, A., Dessì, N., & Pes, B. (2007). Intelligent Data Engineering and Automated Learning - IDEAL 2007. (H. Yin, P. Tino, E. Corchado, W. Byrne, & X. Yao, Eds.), IDEAL (Vol. 4881, pp. 790–799). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-77226-2
Mendeley helps you to discover research relevant for your work.