{"_buckets": {"deposit": "4d081ffc-b6ad-4b01-8c32-dd6572694382"}, "_deposit": {"created_by": 45, "id": "6187", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "6187"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/6187", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Child Face Recognition System Using Mobilefacenet", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Face recognition is a kind of identifying people in image. It matches the database of known faces and input image of unknown face. Deep learning is one of the state-of-art technologies which achieve state-of-art performance on face recognition. In this paper, we develop child face recognition using MobileFaceNet. MobileFaceNet is efficient Convolutional Neural Network (CNN) models and it uses more than 1 million parameters. MobileFaceNet is used for feature extractions. Since MobileFaceNet is one of the types of light weights models, we can apply this face recognition system on mobile and embedded devices. Dlib is used for preprocessing and K-Nearest Neighbors (KNN) is used for classification process. MobileFaceNet is trained by ArcFace loss and it achieve the 96% accuracy on child face dataset."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Face recognition"}, {"interim": "MobileFaceNet"}, {"interim": "Convolutional Neural Network"}, {"interim": "deep learning"}, {"interim": "K-Nearest Neighbors"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-11-17"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": " Child Face Recognition System Using Mobilefacenet.pdf", "filesize": [{"value": "390 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 390000.0, "url": {"url": "https://meral.edu.mm/record/6187/files/ Child Face Recognition System Using Mobilefacenet.pdf"}, "version_id": "c7534279-f348-48a8-9848-deb436e95b6b"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICSTI-IEEE", "subitem_c_date": "September, 2019", "subitem_conference_title": "2019 Joint International Conference on Science, Technology and Innovation, Mandalay by IEEE"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Shun Lei Myat Oo"}, {"subitem_authors_fullname": "Aung Nway Oo"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-09-02"}, "item_title": "Child Face Recognition System Using Mobilefacenet", "item_type_id": "21", "owner": "45", "path": ["1596102355557"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000006187", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-11-17"}, "publish_date": "2020-11-17", "publish_status": "0", "recid": "6187", "relation": {}, "relation_version_is_last": true, "title": ["Child Face Recognition System Using Mobilefacenet"], "weko_shared_id": -1}