PERFORMANCE ANALYSIS OF FACE RECOGNITION ALGORITHMS ON KOREAN FACE DATABASE.
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| Title: | PERFORMANCE ANALYSIS OF FACE RECOGNITION ALGORITHMS ON KOREAN FACE DATABASE. |
|---|---|
| Authors: | MYUNG-CHEOL ROH1 mcroh@image.korea.ac.kr, SEONG-WHAN LEE1 swlee@image.korea.ac.kr |
| Source: | International Journal of Pattern Recognition & Artificial Intelligence. Sep2007, Vol. 21 Issue 6, p1017-1033. 17p. 7 Color Photographs, 1 Diagram, 8 Charts, 9 Graphs. |
| Subjects: | Human facial recognition software, Face perception, Optical pattern recognition, Performance evaluation, Databases, Algorithms |
| Abstract: | Human face is one of the most common and useful keys to a person's identity. Although, a number of face recognition algorithms have been proposed, many researchers believe that the technology should be improved further in order to overcome the instability caused by variable illuminations, expressions, poses and accessories. To analyze these face recognition algorithm, it is indispensable to collect various data as much as possible. Face databases such as CMU PIE (USA), FERET (USA), AR Face DB (USA) and XM2VTS (UK) are the representative ones commonly used. However, many databases do not provide adequately annotated information of the pose angle, illumination angle, illumination color and ground-truth. Mostly, they do not include large enough number of images and video data taken under various environments. Furthermore, the faces on these databases have different characteristics from those of Asian. Thus, we have designed and constructed a Korean Face Database (KFDB) which includes not only images but also video clips, ground-truth information of facial feature points and descriptions of subjects and environment conditions so that it can be used for general purposes. In this paper, we present the KFDB which contains image and video data for 1920 subjects and has been constructed in 3 years (sessions). We also present recognition results by CM (Correlation Matching) and PCA (Principal Component Analysis) which are used as baseline algorithms upon CMU PIE and KFDB, so as to understand how recognition rate is changed by altering image taking conditions. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 26619324 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: PERFORMANCE ANALYSIS OF FACE RECOGNITION ALGORITHMS ON KOREAN FACE DATABASE. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22MYUNG-CHEOL+ROH%22">MYUNG-CHEOL ROH</searchLink><relatesTo>1</relatesTo><i> mcroh@image.korea.ac.kr</i><br /><searchLink fieldCode="AR" term="%22SEONG-WHAN+LEE%22">SEONG-WHAN LEE</searchLink><relatesTo>1</relatesTo><i> swlee@image.korea.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Sep2007, Vol. 21 Issue 6, p1017-1033. 17p. 7 Color Photographs, 1 Diagram, 8 Charts, 9 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Human+facial+recognition+software%22">Human facial recognition software</searchLink><br /><searchLink fieldCode="DE" term="%22Face+perception%22">Face perception</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+pattern+recognition%22">Optical pattern recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Human face is one of the most common and useful keys to a person's identity. Although, a number of face recognition algorithms have been proposed, many researchers believe that the technology should be improved further in order to overcome the instability caused by variable illuminations, expressions, poses and accessories. To analyze these face recognition algorithm, it is indispensable to collect various data as much as possible. Face databases such as CMU PIE (USA), FERET (USA), AR Face DB (USA) and XM2VTS (UK) are the representative ones commonly used. However, many databases do not provide adequately annotated information of the pose angle, illumination angle, illumination color and ground-truth. Mostly, they do not include large enough number of images and video data taken under various environments. Furthermore, the faces on these databases have different characteristics from those of Asian. Thus, we have designed and constructed a Korean Face Database (KFDB) which includes not only images but also video clips, ground-truth information of facial feature points and descriptions of subjects and environment conditions so that it can be used for general purposes. In this paper, we present the KFDB which contains image and video data for 1920 subjects and has been constructed in 3 years (sessions). We also present recognition results by CM (Correlation Matching) and PCA (Principal Component Analysis) which are used as baseline algorithms upon CMU PIE and KFDB, so as to understand how recognition rate is changed by altering image taking conditions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218001407005818 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1017 Subjects: – SubjectFull: Human facial recognition software Type: general – SubjectFull: Face perception Type: general – SubjectFull: Optical pattern recognition Type: general – SubjectFull: Performance evaluation Type: general – SubjectFull: Databases Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: PERFORMANCE ANALYSIS OF FACE RECOGNITION ALGORITHMS ON KOREAN FACE DATABASE. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: MYUNG-CHEOL ROH – PersonEntity: Name: NameFull: SEONG-WHAN LEE IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 02180014 Numbering: – Type: volume Value: 21 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence Type: main |
| ResultId | 1 |
