Face information processing by fast statistical learning algorithm

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2008 International Joint Conference on Neural Networks, IJCNN 2008
ページ3229-3232
ページ数4
DOI
出版ステータスPublished - 2008 11 24
外部発表はい
イベント2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
継続期間: 2008 6 12008 6 8

出版物シリーズ

名前Proceedings 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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