Abstract
This paper explores a synthetic method to create the unseen face features in the database, thus achieving better performance of image set based face recognition. Image set based classification highly depend on the consistency and coverage of the poses and view point variations of a subject in gallery and probe sets. By considering the high symmetry of human faces, multiple synthetic instances are virtually generated to make up the missing parts, so as to enrich the variety of the database. With respect to the classification framework, we resort to reverse training due to its high efficiency and accuracy. Experiments are performed on benchmark datasets containing facial image sequences. Comparisons with state-of-the-art methods have corroborated the superiority of our Synthetic Examples based Reverse Training (SERT) approach.
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Liang, Q., Zhang, L., Li, H., Lu, J. (2015). Image Set Classification Based on Synthetic Examples and Reverse Training. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_30
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DOI: https://doi.org/10.1007/978-3-319-22053-6_30
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