Generalized Low Dimensional Feature Subspace for Robust Face Recognition on Unseen datasets using Kernel Correlation Feature Analysis

In this paper we analyze and demonstrate the subspace generalization power of the kernel correlation feature analysis (KCFA) method for producing compact low dimensional subspace that has good representation ability to work on unseen, untrained datasets. Examining the portability of an algorithm acr...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Abiantun, Ramzi, Savvides, Marios, Vijayakumar, B.V.K.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithm design and analysis Cameras Face recognition FERET Filtering theory Filters FRGC Kernel Kernel Correlation Filters Performance analysis PIE Principal component analysis Reduced Feature Subspace Robustness Testing
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!