publication . Part of book or chapter of book . Conference object . 2009

A Classification Framework for Large-Scale Face Recognition Systems

Ziheng Zhou; Farzin Deravi; Samuel Chindaro;
Open Access
  • Published: 01 Jun 2009
  • Publisher: Springer Berlin Heidelberg
  • Country: United Kingdom
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 classifica...
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: TA1650, Kernel (linear algebra), Fisher kernel, Linear discriminant analysis, Data imbalance, Facial recognition system, Classifier (linguistics), Artificial intelligence, business.industry, business, Classification methods, Pattern recognition, Machine learning, computer.software_genre, computer, Kernel Fisher discriminant analysis, Computer science
Download fromView all 2 versions
Part of book or chapter of book
Provider: UnpayWall
Part of book or chapter of book . 2009
Provider: Crossref
Any information missing or wrong?Report an Issue