Abstract
In this paper a holistic method and a local method based on decision template ensemble are investigated. In addition by combining both methods, a new hybrid method for boosting the performance of the system is proposed and evaluated with respect to robustness against small sample size problem in face recognition. Inadequate and substantial variations in the available training samples are the two challenging obstacles in classification of an unknown face image. At first in this novel multi learner framework, a decision template is designed for the global face and a set of decision templates is constructed for each local part of the face as a complement to the previous part. The prominent results demonstrate that, the new hybrid method based on fusion of weighted multiple decision templates is superior to the other classic combining schemes for both ORL and Yale data sets. In addition when the global and the local components of the face are combined together the best performance is achieved.
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Ghaemi, M.S., Masoudnia, S., Ebrahimpour, R. (2010). A New Framework for Small Sample Size Face Recognition Based on Weighted Multiple Decision Templates. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_58
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DOI: https://doi.org/10.1007/978-3-642-17537-4_58
Publisher Name: Springer, Berlin, Heidelberg
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