MERAL Myanmar Education Research and Learning Portal
Item
{"_buckets": {"deposit": "b6871112-b771-4c74-9ee3-1f7be4adf822"}, "_deposit": {"id": "4452", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4452"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4452", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Automatic Age Prediction of Aging Effects on Face Images", "subitem_1551255648112": "en_US"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "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."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Age Prediction"}, {"interim": "Feature Extraction"}, {"interim": "Principal component Analysis (PCA)"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-11-13"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "10016.pdf", "filesize": [{"value": "499 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 499000.0, "url": {"url": "https://meral.edu.mm/record/4452/files/10016.pdf"}, "version_id": "d99c16f7-5286-4412-a03e-8b6db9e1883c"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Tenth International Conference On Computer Applications (ICCA 2012)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Tin, Hlaing Htake Khaung"}, {"subitem_authors_fullname": "Sein, Myint Myint"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2012-02-28"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/2383"}, "item_title": "Automatic Age Prediction of Aging Effects on Face Images", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004452", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-11-13"}, "publish_date": "2019-11-13", "publish_status": "0", "recid": "4452", "relation": {}, "relation_version_is_last": true, "title": ["Automatic Age Prediction of Aging Effects on Face Images"], "weko_shared_id": -1}
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 |
---|---|---|
10016.pdf (499 Kb)
|
|
Publication type | ||||||
---|---|---|---|---|---|---|
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 |