Face information processing by fast statistical learning algorithm

M. Nakano, S. Karungaru, S. Tsuge, T. Akashi, Yasue 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 publicationProceedings of the International Joint Conference on Neural Networks
Pages3229-3232
Number of pages4
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 2008 Jun 12008 Jun 8

Other

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

Fingerprint

Learning algorithms
Feature extraction
Discriminant analysis
Principal component analysis
Computer simulation
Processing

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Nakano, M., Karungaru, S., Tsuge, S., Akashi, T., Mitsukura, Y., & Fukumi, M. (2008). Face information processing by fast statistical learning algorithm. In Proceedings of the International Joint Conference on Neural Networks (pp. 3229-3232). [4634256] https://doi.org/10.1109/IJCNN.2008.4634256

Face information processing by fast statistical learning algorithm. / Nakano, M.; Karungaru, S.; Tsuge, S.; Akashi, T.; Mitsukura, Yasue; Fukumi, M.

Proceedings of the International Joint Conference on Neural Networks. 2008. p. 3229-3232 4634256.

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

Nakano, M, Karungaru, S, Tsuge, S, Akashi, T, Mitsukura, Y & Fukumi, M 2008, Face information processing by fast statistical learning algorithm. in Proceedings of the International Joint Conference on Neural Networks., 4634256, pp. 3229-3232, 2008 International Joint Conference on Neural Networks, IJCNN 2008, Hong Kong, China, 08/6/1. https://doi.org/10.1109/IJCNN.2008.4634256
Nakano M, Karungaru S, Tsuge S, Akashi T, Mitsukura Y, Fukumi M. Face information processing by fast statistical learning algorithm. In Proceedings of the International Joint Conference on Neural Networks. 2008. p. 3229-3232. 4634256 https://doi.org/10.1109/IJCNN.2008.4634256
Nakano, M. ; Karungaru, S. ; Tsuge, S. ; Akashi, T. ; Mitsukura, Yasue ; Fukumi, M. / Face information processing by fast statistical learning algorithm. Proceedings of the International Joint Conference on Neural Networks. 2008. pp. 3229-3232
@inproceedings{8a90cdaa34154c91934b01eb803e407d,
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. 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.",
author = "M. Nakano and S. Karungaru and S. Tsuge and T. Akashi and Yasue Mitsukura and M. Fukumi",
year = "2008",
doi = "10.1109/IJCNN.2008.4634256",
language = "English",
isbn = "9781424418213",
pages = "3229--3232",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",

}

TY - GEN

T1 - Face information processing by fast statistical learning algorithm

AU - Nakano, M.

AU - Karungaru, S.

AU - Tsuge, S.

AU - Akashi, T.

AU - Mitsukura, Yasue

AU - Fukumi, M.

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=56349105619&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=56349105619&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2008.4634256

DO - 10.1109/IJCNN.2008.4634256

M3 - Conference contribution

AN - SCOPUS:56349105619

SN - 9781424418213

SP - 3229

EP - 3232

BT - Proceedings of the International Joint Conference on Neural Networks

ER -