Face recognition using support vector machines with the feature set extracted by genetic algorithms

Kyunghee Lee; Yongwha Chung; Hyeran Byun

doi:10.1007/3-540-45344-x_5

Face recognition using support vector machines with the feature set extracted by genetic algorithms

Kyunghee Lee, Yongwha Chung, Hyeran Byun

College of Computing

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

3 Citations (Scopus)

Abstract

Face recognition problem is challenging because face images can vary considerably in terms of facial expressions, 3D orientation, lighting conditions, hair styles, and so on. This paper proposes a method of face recognition by using support vector machines with the feature set extracted by genetic algorithms. By selecting the feature set that has superior performance in recognizing faces, the use of unnecessary information of the faces can be avoided and the memory requirement can be decreased significantly. Also, by using a tuning data set in the computation of the evaluation function, the feature set which is less dependent on illumination and expression can be selected. The experimental results show that the proposed method can provide superior performance than the previous method in terms of accuracy and memory requirement.

Original language	English
Title of host publication	Audio- and Video-Based Biometric Person Authentication - Third International Conference, AVBPA 2001, Proceedings
Publisher	Springer Verlag
Pages	32-37
Number of pages	6
ISBN (Print)	3540422161, 9783540422167
DOIs	https://doi.org/10.1007/3-540-45344-x_5
Publication status	Published - 2001
Event	3rd International Conference on Audio- and Video- Based Biometric Person Authentication, AVBPA 2001 - Halmstad, Sweden Duration: 2001 Jun 6 → 2001 Jun 8

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2091 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Conference on Audio- and Video- Based Biometric Person Authentication, AVBPA 2001
Country/Territory	Sweden
City	Halmstad
Period	01/6/6 → 01/6/8

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45344-x_5

Cite this

Lee, K., Chung, Y., & Byun, H. (2001). Face recognition using support vector machines with the feature set extracted by genetic algorithms. In Audio- and Video-Based Biometric Person Authentication - Third International Conference, AVBPA 2001, Proceedings (pp. 32-37). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2091 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-45344-x_5

Lee, Kyunghee ; Chung, Yongwha ; Byun, Hyeran. / Face recognition using support vector machines with the feature set extracted by genetic algorithms. Audio- and Video-Based Biometric Person Authentication - Third International Conference, AVBPA 2001, Proceedings. Springer Verlag, 2001. pp. 32-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Face recognition using support vector machines with the feature set extracted by genetic algorithms",

abstract = "Face recognition problem is challenging because face images can vary considerably in terms of facial expressions, 3D orientation, lighting conditions, hair styles, and so on. This paper proposes a method of face recognition by using support vector machines with the feature set extracted by genetic algorithms. By selecting the feature set that has superior performance in recognizing faces, the use of unnecessary information of the faces can be avoided and the memory requirement can be decreased significantly. Also, by using a tuning data set in the computation of the evaluation function, the feature set which is less dependent on illumination and expression can be selected. The experimental results show that the proposed method can provide superior performance than the previous method in terms of accuracy and memory requirement.",

author = "Kyunghee Lee and Yongwha Chung and Hyeran Byun",

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note = "3rd International Conference on Audio- and Video- Based Biometric Person Authentication, AVBPA 2001 ; Conference date: 06-06-2001 Through 08-06-2001",

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Lee, K, Chung, Y & Byun, H 2001, Face recognition using support vector machines with the feature set extracted by genetic algorithms. in Audio- and Video-Based Biometric Person Authentication - Third International Conference, AVBPA 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2091 LNCS, Springer Verlag, pp. 32-37, 3rd International Conference on Audio- and Video- Based Biometric Person Authentication, AVBPA 2001, Halmstad, Sweden, 01/6/6. https://doi.org/10.1007/3-540-45344-x_5

Face recognition using support vector machines with the feature set extracted by genetic algorithms. / Lee, Kyunghee; Chung, Yongwha; Byun, Hyeran.

Audio- and Video-Based Biometric Person Authentication - Third International Conference, AVBPA 2001, Proceedings. Springer Verlag, 2001. p. 32-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2091 LNCS).

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

TY - GEN

T1 - Face recognition using support vector machines with the feature set extracted by genetic algorithms

AU - Lee, Kyunghee

AU - Chung, Yongwha

AU - Byun, Hyeran

PY - 2001

Y1 - 2001

N2 - Face recognition problem is challenging because face images can vary considerably in terms of facial expressions, 3D orientation, lighting conditions, hair styles, and so on. This paper proposes a method of face recognition by using support vector machines with the feature set extracted by genetic algorithms. By selecting the feature set that has superior performance in recognizing faces, the use of unnecessary information of the faces can be avoided and the memory requirement can be decreased significantly. Also, by using a tuning data set in the computation of the evaluation function, the feature set which is less dependent on illumination and expression can be selected. The experimental results show that the proposed method can provide superior performance than the previous method in terms of accuracy and memory requirement.

AB - Face recognition problem is challenging because face images can vary considerably in terms of facial expressions, 3D orientation, lighting conditions, hair styles, and so on. This paper proposes a method of face recognition by using support vector machines with the feature set extracted by genetic algorithms. By selecting the feature set that has superior performance in recognizing faces, the use of unnecessary information of the faces can be avoided and the memory requirement can be decreased significantly. Also, by using a tuning data set in the computation of the evaluation function, the feature set which is less dependent on illumination and expression can be selected. The experimental results show that the proposed method can provide superior performance than the previous method in terms of accuracy and memory requirement.

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M3 - Conference contribution

AN - SCOPUS:67649122136

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SN - 9783540422167

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Audio- and Video-Based Biometric Person Authentication - Third International Conference, AVBPA 2001, Proceedings

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Lee K, Chung Y, Byun H. Face recognition using support vector machines with the feature set extracted by genetic algorithms. In Audio- and Video-Based Biometric Person Authentication - Third International Conference, AVBPA 2001, Proceedings. Springer Verlag. 2001. p. 32-37. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45344-x_5

Face recognition using support vector machines with the feature set extracted by genetic algorithms

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