Paper Info
Title
Face Recognition using Layered Linear Discriminant Analysis and Small Subspace
Abstract
Face recognition has great demands in human recognition and recently it becomes one of the most important research areas of biometrics. In this paper, we present a novel layered face recognition method based on Fisher’s linear discriminant analysis. The basic aim is to decrease FAR by reducing the face dataset to small size by applying layered linear discriminant analysis. Although, the computational complexity at the time of recognition is much higher than conventional PCA and LDA due to the weights computation for small subspace at the time of recognition, but on the other hand the layered LDA provides significant performance gain especially on similar face database. Layered LDA is insensitive to large dataset and also small sample size and it provides 93% accuracy on BANCA face database. Experimental and simulation results show that the proposed scheme has encouraging results for a practical face recognition system.
Year
DOI
Venue
2010
10.1109/CIT.2010.252
CIT
Keywords
Field
DocType
face recognition,face dataset,layered linear discriminant analysis,banca face database,practical face recognition system,layered lda,novel layered face recognition,human recognition,small subspace,small sample size,similar face database,databases,biometrics,face,principal component analysis,accuracy,statistical analysis,computational complexity,linear discriminant analysis,pca
Facial recognition system,Pattern recognition,Subspace topology,Computer science,Artificial intelligence,Biometrics,Linear discriminant analysis,Principal component analysis,Sample size determination,Computational complexity theory,Computation
Conference
Citations 
PageRank 
References 
8
0.61
9
Authors
4
Name
Order
Citations
PageRank
Muhammad Imran Razzak122133.86
Muhammad Khurram Khan23538204.81
Khaled Alghathbar349832.54
Rubiyah Yousaf4110.99