Abstract
Most of current machine learning methods used in face recognition systems require sufficient data to build a face model or face data description. However insufficient data is currently a common issue. This paper presents a new learning approach to tackle this issue. The proposed learning method employs not only the data in facial images but also relations between them to build relational face models. Preliminary experiments performed on the AT&T and FERET face corpus show a significant improvement for face recognition rate when only a small facial data set is available for training.
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Bui, L., Tran, D., Huang, X., Chetty, G. (2011). Relation Learning - A New Approach to Face Recognition. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_51
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DOI: https://doi.org/10.1007/978-3-642-23687-7_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23686-0
Online ISBN: 978-3-642-23687-7
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