Feature selection for iris recognition with AdaBoost

Kan Ru Chen; Chia Te Chou; Sheng Wen Shih; Wen Shiung Chen; Duan Yu Chen

doi:10.1109/IIHMSP.2007.4457736

Feature selection for iris recognition with AdaBoost

Kan Ru Chen, Chia Te Chou, Sheng Wen Shih, Wen Shiung Chen, Duan Yu Chen

Center for General Education

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

In this paper, we proposed a method for selecting edge-type features for iris recognition. The AdaBoost algorithm is used to select a filter bank from a pile of filter candidates. The decisions of the weak classifiers associated with the filter bank are linearly combined to form a strong classifier. Real experiments have been conducted to assess the performance of the designed strong classifier. The results showed that the boosting algorithm can effectively improve the recognition accuracy at the cost of slightly increase the computation time.

Original language	English
Title of host publication	Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.
Pages	411-414
Number of pages	4
DOIs	https://doi.org/10.1109/IIHMSP.2007.4457736
Publication status	Published - 2007
Event	3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007 - Kaohsiung, Taiwan Duration: Nov 26 2007 → Nov 28 2007

Publication series

Name	Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.
Volume	2

Conference

Conference	3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007
Country/Territory	Taiwan
City	Kaohsiung
Period	11/26/07 → 11/28/07

Keywords

AdaBoost
Biometrics
Feature extraction
Iris recognition

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Information Systems and Management

Access to Document

10.1109/IIHMSP.2007.4457736

Cite this

Chen, K. R., Chou, C. T., Shih, S. W., Chen, W. S., & Chen, D. Y. (2007). Feature selection for iris recognition with AdaBoost. In Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007. (pp. 411-414). Article 4457736 (Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.; Vol. 2). https://doi.org/10.1109/IIHMSP.2007.4457736

Feature selection for iris recognition with AdaBoost. / Chen, Kan Ru; Chou, Chia Te; Shih, Sheng Wen et al.
Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.. 2007. p. 411-414 4457736 (Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, KR, Chou, CT, Shih, SW, Chen, WS & Chen, DY 2007, Feature selection for iris recognition with AdaBoost. in Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.., 4457736, Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007., vol. 2, pp. 411-414, 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007, Kaohsiung, Taiwan, 11/26/07. https://doi.org/10.1109/IIHMSP.2007.4457736

Chen KR, Chou CT, Shih SW, Chen WS, Chen DY. Feature selection for iris recognition with AdaBoost. In Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.. 2007. p. 411-414. 4457736. (Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.). doi: 10.1109/IIHMSP.2007.4457736

Chen, Kan Ru ; Chou, Chia Te ; Shih, Sheng Wen et al. / Feature selection for iris recognition with AdaBoost. Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.. 2007. pp. 411-414 (Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.).

@inproceedings{f725ed0c7c984038bcb88b8f0bd83178,

title = "Feature selection for iris recognition with AdaBoost",

abstract = "In this paper, we proposed a method for selecting edge-type features for iris recognition. The AdaBoost algorithm is used to select a filter bank from a pile of filter candidates. The decisions of the weak classifiers associated with the filter bank are linearly combined to form a strong classifier. Real experiments have been conducted to assess the performance of the designed strong classifier. The results showed that the boosting algorithm can effectively improve the recognition accuracy at the cost of slightly increase the computation time.",

keywords = "AdaBoost, Biometrics, Feature extraction, Iris recognition",

author = "Chen, {Kan Ru} and Chou, {Chia Te} and Shih, {Sheng Wen} and Chen, {Wen Shiung} and Chen, {Duan Yu}",

year = "2007",

doi = "10.1109/IIHMSP.2007.4457736",

language = "English",

isbn = "0769529941",

series = "Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.",

pages = "411--414",

booktitle = "Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.",

note = "3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007 ; Conference date: 26-11-2007 Through 28-11-2007",

}

TY - GEN

T1 - Feature selection for iris recognition with AdaBoost

AU - Chen, Kan Ru

AU - Chou, Chia Te

AU - Shih, Sheng Wen

AU - Chen, Wen Shiung

AU - Chen, Duan Yu

PY - 2007

Y1 - 2007

N2 - In this paper, we proposed a method for selecting edge-type features for iris recognition. The AdaBoost algorithm is used to select a filter bank from a pile of filter candidates. The decisions of the weak classifiers associated with the filter bank are linearly combined to form a strong classifier. Real experiments have been conducted to assess the performance of the designed strong classifier. The results showed that the boosting algorithm can effectively improve the recognition accuracy at the cost of slightly increase the computation time.

AB - In this paper, we proposed a method for selecting edge-type features for iris recognition. The AdaBoost algorithm is used to select a filter bank from a pile of filter candidates. The decisions of the weak classifiers associated with the filter bank are linearly combined to form a strong classifier. Real experiments have been conducted to assess the performance of the designed strong classifier. The results showed that the boosting algorithm can effectively improve the recognition accuracy at the cost of slightly increase the computation time.

KW - AdaBoost

KW - Biometrics

KW - Feature extraction

KW - Iris recognition

UR - http://www.scopus.com/inward/record.url?scp=47349123255&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=47349123255&partnerID=8YFLogxK

U2 - 10.1109/IIHMSP.2007.4457736

DO - 10.1109/IIHMSP.2007.4457736

M3 - Conference contribution

AN - SCOPUS:47349123255

SN - 0769529941

SN - 9780769529943

T3 - Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.

SP - 411

EP - 414

BT - Proceedings - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007.

T2 - 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007

Y2 - 26 November 2007 through 28 November 2007

ER -

Feature selection for iris recognition with AdaBoost

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this