Presentation 2010-03-15
Gradient Features for Accuracy Improvement of Face Image Verification and Recognition
Yushi KOMAKI, Wataru OHYAMA, Tetsushi WAKABAYASHI, Fumitaka KIMURA,
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Abstract(in English) We proposed a method that describes face appearance using gradient features for appearance based face identification using frontal face images. A person who was registered before is called "genuine" and a person who was not registered is called "impostor". Verification is a task which classifies a person in an input image as an insider or an outsider. And the person classified into insider is sent to the recognition process. The recognition is a task which recognizes who the person is. Either proposed gradient features or conventional grayscale features is extracted from frontal face image, the two methods are compared. In the experiment, we use frontal face images taken under three different situations: (1) different facial expression, (2) different illumination, (3) facial appearance change by aging. In verification and recognition, higher performatnce of gradient features is confirmed than grayscale features in all three situations. Equal Error Rate has been reduced from 27.88% to 15.93%. Recognition Error Rate has been reduced from 8.99% to 5.22%. The reason is that variation of gradient features by variation of facial location in an image, form change of facial parts, and global illumination change is small.
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Keyword(in English) Biometrics / Face Image Identification / Verification / Recognition / Gradient Features
Paper # PRMU2009-244,HIP2009-129
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Conference Information
Committee PRMU
Conference Date 2010/3/8(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Gradient Features for Accuracy Improvement of Face Image Verification and Recognition
Sub Title (in English)
Keyword(1) Biometrics
Keyword(2) Face Image Identification
Keyword(3) Verification
Keyword(4) Recognition
Keyword(5) Gradient Features
1st Author's Name Yushi KOMAKI
1st Author's Affiliation Graduate School of Engineering, Mie University()
2nd Author's Name Wataru OHYAMA
2nd Author's Affiliation Graduate School of Engineering, Mie University
3rd Author's Name Tetsushi WAKABAYASHI
3rd Author's Affiliation Graduate School of Engineering, Mie University
4th Author's Name Fumitaka KIMURA
4th Author's Affiliation Graduate School of Engineering, Mie University
Date 2010-03-15
Paper # PRMU2009-244,HIP2009-129
Volume (vol) vol.109
Number (no) 470
Page pp.pp.-
#Pages 6
Date of Issue