A new framework for small sample size face recognition based on weighted multiple decision templates

Authors:
Mohammad Sajjad Ghaemi;Saeed Masoudnia;Reza Ebrahimpour
Affiliations:
Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran and Research Center of Intelligent Signal Processing, Tehran, Iran;Islamic Azad University, Tehran, Iran;School of Cognitive Sciences, Institute for Studies on Theoretical Physics and Mathematics, Tehran, Iran and Department of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran ...
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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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.