Paper Info
Title
Face Information Processing By Fast Statistical Learning Algorithm
Abstract
In this paper, we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA. First of all, the approximated principal component analysis (learning by Simple-PCA) that uses the mean vector of each class is calculated. Next, in order to adjust within-class variance in each class to 0, the vectors in the class are removed. By this processing, it becomes high-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images.
Year
DOI
Venue
2008
10.1109/IJCNN.2008.4634256
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8
Keywords
Field
DocType
learning artificial intelligence,pattern recognition,information processing,principal component analysis,data compression,covariance matrix,image recognition,face recognition,face,minimization,accuracy,kernel,feature extraction,computer simulation
Dimensionality reduction,Computer science,Artificial intelligence,Population-based incremental learning,k-nearest neighbors algorithm,Facial recognition system,Feature vector,Pattern recognition,Algorithm,Feature extraction,Linear discriminant analysis,Machine learning,Principal component analysis
Conference
ISSN
Citations 
PageRank 
2161-4393
0
0.34
References 
Authors
6
6
Name
Order
Citations
PageRank
Miyoko Nakano182.03
Stephen Karungaru2149.91
Satoru Tsuge34013.20
Takuya Akashi4209.57
Yasue Mitsukura516347.48
Minoru Fukumi614649.05