N-division output coding method applied to face recognition

Jaepil Ko; Hyeran Byun

doi:10.1016/S0167-8655(03)00179-X

N-division output coding method applied to face recognition

Jaepil Ko, Hyeran Byun

College of Computing

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

Most research on face recognition has focused on representation of face appearances rather than the classifiers. For robust classification performance, we need to adopt elaborate classifiers. Output coding is suitable for this purpose because it can allow online learning. In this paper, we propose an N-division output coding method. In the experiments we demonstrate such properties as problem complexity, margin of separation, machine relevance and the recognition performance among different output coding methods.

Original language	English
Pages (from-to)	3115-3123
Number of pages	9
Journal	Pattern Recognition Letters
Volume	24
Issue number	16
DOIs	https://doi.org/10.1016/S0167-8655(03)00179-X
Publication status	Published - 2003 Dec

Bibliographical note

Funding Information:
This work was supported (in part) by Korea Science and Engineering Foundation (KOSEF) through Biometrics Engineering Research Center (BERC) at Yonsei University.

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/S0167-8655(03)00179-X

Cite this

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title = "N-division output coding method applied to face recognition",

abstract = "Most research on face recognition has focused on representation of face appearances rather than the classifiers. For robust classification performance, we need to adopt elaborate classifiers. Output coding is suitable for this purpose because it can allow online learning. In this paper, we propose an N-division output coding method. In the experiments we demonstrate such properties as problem complexity, margin of separation, machine relevance and the recognition performance among different output coding methods.",

author = "Jaepil Ko and Hyeran Byun",

note = "Funding Information: This work was supported (in part) by Korea Science and Engineering Foundation (KOSEF) through Biometrics Engineering Research Center (BERC) at Yonsei University. ",

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AU - Byun, Hyeran

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N2 - Most research on face recognition has focused on representation of face appearances rather than the classifiers. For robust classification performance, we need to adopt elaborate classifiers. Output coding is suitable for this purpose because it can allow online learning. In this paper, we propose an N-division output coding method. In the experiments we demonstrate such properties as problem complexity, margin of separation, machine relevance and the recognition performance among different output coding methods.

AB - Most research on face recognition has focused on representation of face appearances rather than the classifiers. For robust classification performance, we need to adopt elaborate classifiers. Output coding is suitable for this purpose because it can allow online learning. In this paper, we propose an N-division output coding method. In the experiments we demonstrate such properties as problem complexity, margin of separation, machine relevance and the recognition performance among different output coding methods.

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ER -

N-division output coding method applied to face recognition

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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