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A Classification Framework for Large-Scale Face Recognition Systems

Zhou, Ziheng, Deravi, Farzin (2009) A Classification Framework for Large-Scale Face Recognition Systems. In: 3rd IAPR/IEEE International Conference on Biometrics, 2-5 June, University of Sassari, Italy. (doi:10.1007/978-3-642-01793-3_35) (KAR id:23302)

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This paper presents a generic classification framework for large-scale face recognition systems. Within the framework, a data sampling strategy is proposed to tackle the data imbalance when image pairs are sampled from thousands of face images for preparing a training dataset. A modified kernel Fisher discriminant classifier is proposed to make it computationally feasible to train the kernel-based classification method using tens of thousands of training samples. The framework is tested in an open-set face recognition scenario and the performance of the proposed classifier is compared with alternative techniques. The experimental results show that the classification framework can effectively manage large amounts of training data, without regard to feature types, to efficiently train classifiers with high recognition accuracy compared to alternative techniques.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-3-642-01793-3_35
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1653 Human face recognition
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: J. Harries
Date Deposited: 29 Jun 2011 13:02 UTC
Last Modified: 16 Nov 2021 10:01 UTC
Resource URI: (The current URI for this page, for reference purposes)
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