{"_buckets": {"deposit": "8ca7c58c-3a67-4a61-a9cd-3007fda2e0c8"}, "_deposit": {"created_by": 45, "id": "6635", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "6635"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00006635", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Evaluation of Face Recognition Techniques for Facial Expression Analysis", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Face recognition is an important area in the field of\nbiometrics. It has been an active area of research for\nseveral decades, but still remains a challenging problem\nbecause of the complexity of the human face. Many\nrecognition methods have been proposed, however, most\nof them are not able to make use of local salient features\nto effectively capture the face information. Generally, the\nperformance of face recognition system is determined by\nextracting feature vector exactly and classifying them into\na class accurately. Therefore, it is necessary to pay\nattention to feature extraction method and classifier. In\nthis paper, we compare and analyze the Principle\nComponent Analysis (PCA), Two Dimensional Principle\nComponent Analysis (2DPCA) and Histogram of Oriented\nGradients (HOG) based on the recognition rate and\naccess time from the experimental results. The experiment\nis done on three sets of databases: the AT\u0026T, Yale and\nown created face database."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Face Recognition"}, {"interim": "Evaluation"}, {"interim": "HOG"}, {"interim": "PCA"}, {"interim": "2DPCA"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-11-26"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Evaluation of Face Recognition Techniques for Facial Expression Analysis.pdf", "filesize": [{"value": "766 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 766000.0, "url": {"url": "https://meral.edu.mm/api/files/8ca7c58c-3a67-4a61-a9cd-3007fda2e0c8/Evaluation%20of%20Face%20Recognition%20Techniques%20for%20Facial%20Expression%20Analysis.pdf"}, "version_id": "e0f5aeff-5162-47d3-b750-da2e05ea8832"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICAIT-2017", "subitem_c_date": "1-2 November, 2017", "subitem_conference_title": "1st International Conference on Advanced Information Technologies", "subitem_place": "Yangon, Myanmar", "subitem_session": "Workshop Session", "subitem_website": "https://www.uit.edu.mm/icait-2017/"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Hla Myat Maw"}, {"subitem_authors_fullname": "K Zin Lin"}, {"subitem_authors_fullname": "Myat Thida Mon"}]}]}, "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": "2017-11-02"}, "item_title": "Evaluation of Face Recognition Techniques for Facial Expression Analysis", "item_type_id": "21", "owner": "45", "path": ["1605779935331"], "permalink_uri": "https://meral.edu.mm/records/6635", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-11-26"}, "publish_date": "2020-11-26", "publish_status": "0", "recid": "6635", "relation": {}, "relation_version_is_last": true, "title": ["Evaluation of Face Recognition Techniques for Facial Expression Analysis"], "weko_shared_id": -1}