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Kyaukse University
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Mohnyin University
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Myanmar Institute of Information Technology
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Myanmar Maritime University
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National Management Degree College
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Naypyitaw State Academy
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Pathein University
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Sagaing University
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Sagaing University of Education
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Taunggyi University
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Technological University, Hmawbi
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Technological University (Kyaukse)
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Technological University Mandalay
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University of Computer Studies, Mandalay
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University of Computer Studies, Meikhtila
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University of Computer Studies, Taungoo
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University of Computer Studies, Yangon
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University of Dental Medicine Mandalay
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University of Dental Medicine, Yangon
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University of Information Technology
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University of Mandalay
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University of Medicine 1
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University of Public Health, Yangon
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Yangon University of Economics
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Yangon University of Education
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Yangon University of Foreign Languages
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Yezin Agricultural University
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New Index
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Item
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Automatic Age Prediction of Aging Effects on Face Images
http://hdl.handle.net/20.500.12678/0000004452
http://hdl.handle.net/20.500.12678/000000445247247171-7ae2-4f3f-a3cb-70841b7aff33
b6871112-b771-4c74-9ee3-1f7be4adf822
Name / File | License | Actions |
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Publication type | ||||||
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Automatic Age Prediction of Aging Effects on Face Images | |||||
Language | en_US | |||||
Publication date | 2012-02-28 | |||||
Authors | ||||||
Tin, Hlaing Htake Khaung | ||||||
Sein, Myint Myint | ||||||
Description | ||||||
Automatic age prediction system for grayscale facial images is proposed in this paper. Tenage groups, including, are used in the predictionsystem. The process of the system is divided intothree phases: location, feature extraction, andage prediction. Principal Component Analysis(PCA) was used to reduce dimension andenhance class. Finally Euclidean distance wasused to classify the images into one of sevenmajor groups. These groups are: Group1 (0 to10 years), Group2 (11 to 20 years), Group3 (21to 30 years), Group4 (31 to 40 years), Group5(41 to 50 years), Group6 (51 to 60 years) andGroup7 (60 over). The proposed system isexperimented with 1300 facial images on a Core2 Duo processor with 2 GB RAM. One half of theimages are used for training and the other halffor test. It takes 0.2 second to classify an imageon an average. The identification rate achieves95.5% for the training images and 85.5% for thetest images, which is roughly close to human’ssubjective prediction. | ||||||
Keywords | ||||||
Age Prediction, Feature Extraction, Principal component Analysis (PCA) | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/2383 | |||||
Journal articles | ||||||
Tenth International Conference On Computer Applications (ICCA 2012) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |