A study of the dimensionality of the Face Authentication problem using Principal Component Analysis (PCA) and a novel dimensionality reduction algorithm that we call Support Vector Features (SVFs) is presented. Starting from a Gabor feature space, we show that PCA and SVFs identify distinct subspaces with comparable authentication and generalisation performance. Experiments using KNN classifiers and Support Vector Machines (SVMs) on these reduced feature spaces show that the dimensionality at which saturation of the authentication performance is achieved heavily depends on the choice of the classifier. In particular, SVMs involve directions in feature space that carry little variance and therefore appear to be vulnerable to excessive PCA-based compression.
CITATION STYLE
Smeraldi, F., Bigun, J., & Gerstner, W. (2002). Support vector features and the role of dimensionality in face authentication. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2388, pp. 249–259). Springer Verlag. https://doi.org/10.1007/3-540-45665-1_19
Mendeley helps you to discover research relevant for your work.