Face information processing by fast statistical learning algorithm

M. Nakano, S. Karungaru, S. Tsuge, T. Akashi, Y. Mitsukura, M. Fukumi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. Smple-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 Smple-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 Smple-FLDA. The effectiveness is verified by computer simulations using face images.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages3229-3232
Number of pages4
DOIs
Publication statusPublished - 2008 Nov 24
Externally publishedYes
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 2008 Jun 12008 Jun 8

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period08/6/108/6/8

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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