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Prototype-based class-specific nonlinear subspace learning for large-scale face verification

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA)
Number of pages6
ISBN (Electronic)978-1-4673-8910-5
ISBN (Print)978-1-4673-8911-2
Publication statusPublished - Dec 2016
Publication typeA4 Article in a conference publication
EventInternational Conference on Image Processing Theory, Tools and Applications -
Duration: 1 Jan 1900 → …

Publication series

ISSN (Electronic)2154-512X


ConferenceInternational Conference on Image Processing Theory, Tools and Applications
Period1/01/00 → …


In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show the effectiveness of the proposed approach, since it scales well with the data size and outperforms related approaches.


  • Face, Face recognition, Kernel, Optimization, Prototypes, Training, Training data, Class-Specific Discriminant Analysis, Nonlinear Subspace Learning, Prototype-based Approximation

Publication forum classification

Field of science, Statistics Finland