Face information processing by fast statistical learning algorithm

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

doi:10.1109/IJCNN.2008.4634256

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 proceeding › Conference 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 language	English
Title of host publication	2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages	3229-3232
Number of pages	4
DOIs	https://doi.org/10.1109/IJCNN.2008.4634256
Publication status	Published - 2008
Externally published	Yes
Event	2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China Duration: 2008 Jun 1 → 2008 Jun 8

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2008 International Joint Conference on Neural Networks, IJCNN 2008
Country/Territory	China
City	Hong Kong
Period	08/6/1 → 08/6/8

ASJC Scopus subject areas

Software
Artificial Intelligence

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10.1109/IJCNN.2008.4634256

Cite this

Nakano, M, Karungaru, S, Tsuge, S, Akashi, T, Mitsukura, Y & Fukumi, M 2008, Face information processing by fast statistical learning algorithm. in 2008 International Joint Conference on Neural Networks, IJCNN 2008., 4634256, Proceedings of the International Joint Conference on Neural Networks, 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

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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 Y. Mitsukura and M. Fukumi",

year = "2008",

doi = "10.1109/IJCNN.2008.4634256",

language = "English",

isbn = "9781424418213",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "3229--3232",

booktitle = "2008 International Joint Conference on Neural Networks, IJCNN 2008",

note = "2008 International Joint Conference on Neural Networks, IJCNN 2008 ; Conference date: 01-06-2008 Through 08-06-2008",

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T1 - Face information processing by fast statistical learning algorithm

AU - Nakano, M.

AU - Karungaru, S.

AU - Tsuge, S.

AU - Akashi, T.

AU - Mitsukura, Y.

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.

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DO - 10.1109/IJCNN.2008.4634256

M3 - Conference contribution

AN - SCOPUS:56349105619

SN - 9781424418213

T3 - Proceedings of the International Joint Conference on Neural Networks

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EP - 3232

BT - 2008 International Joint Conference on Neural Networks, IJCNN 2008

T2 - 2008 International Joint Conference on Neural Networks, IJCNN 2008

Y2 - 1 June 2008 through 8 June 2008

ER -

Face information processing by fast statistical learning algorithm

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