{"_buckets": {"deposit": "1a7a679b-fcff-4cb2-a106-4f01aebc97e3"}, "_deposit": {"id": "2195", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "2195"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/2195", "sets": ["user-uy"]}, "communities": ["ccm", "ccp", "kyauksetu", "ltc", "maas", "miit", "mlmu", "mmu", "mtlu", "mtu", "mub", "mude", "mufl", "pathein", "scu", "suoe", "tcu", "tgu", "tuh", "tum", "ucsm", "ucsmtla", "ucsmub", "ucspathein", "ucstaungoo", "ucsy", "udmm", "udmy", "uit", "um", "um1", "um2", "umkn", "umm", "uphy", "urj", "uvs", "uy", "yau", "ydbu", "ytu", "yude", "yueco", "yufl", "yuoe"], "control_number": "2195", "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "PALM VEIN RECOGNITION SYSTEM USING DIRECTIONAL CODING AND BACKPROPAGATION NEURAL NETWORK", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "This paper proposes an effective palm vein recognition system by using back-propagation neural networks for biometric application. In the recent years, because of the high development cost, vein pattern is not popular biometric as compared to other biometric system like fingerprint, palm print, face and iris. But, the advantage of palm vein on classical biometric are the low risk of falsification, uniqueness, strong immunity to forge and stability.\r Biometric palm vein images are acquired using near infrared illuminated LEDs and IR-Sensitive web camera from 40 persons of different gender and ages. Firstly, the palm vein region of interest (ROI) was extracted from hand images and then applied with gamma correction and local ridge enhancements (LRE) that were applied to the 100 x 100 pixels image and palm vein pattern images in order obtain the correct contrast and sharpness of the image without excessively increasing the noise. The palm vein features were extracted from the enhanced region of interest for each sample using Sobel directional coding scheme in the four directions (0º, 45º, 90º, 135º). The extracted sobel images were converted to gray-scale image using Otsu\u0027s thresholding method. The resulting gray-scale images were divided into 20x20 sub-regions before the feature matching. Mean absolute deviation (MAD) is implemented to these sub-region as the feature vectors. Those feature sets is the input on the back-propagation neural network. According to the results, the feature matching method can achieve up to 98.75% of correct classification rates."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Biometrics"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-05-05"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Palm Vein recognition system using directional coding and backpropagation Neural Network.pdf", "filesize": [{"value": "844 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 844000.0, "url": {"url": "https://meral.edu.mm/record/2195/files/Palm Vein recognition system using directional coding and backpropagation Neural Network.pdf"}, "version_id": "066b214c-a5c9-4bb7-a811-a15eb48f4408"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_journal_title": "8th AUN/SEED-Net Regional Conference on Electrical and Electronics Engineering"}]}, "item_1583103147082": {"attribute_name": "Conference papaers", "attribute_value_mlt": [{}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_supervisor(s)": []}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Villarin͂a, Mark Erwin C."}, {"subitem_authors_fullname": "Linsangan, Noel B."}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Other"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Other"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2015"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "https://uyr.uy.edu.mm/handle/123456789/387"}, "item_title": "PALM VEIN RECOGNITION SYSTEM USING DIRECTIONAL CODING AND BACKPROPAGATION NEURAL NETWORK", "item_type_id": "21", "owner": "1", "path": ["1582967549708"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000002195", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-03-05"}, "publish_date": "2020-03-05", "publish_status": "0", "recid": "2195", "relation": {}, "relation_version_is_last": true, "title": ["PALM VEIN RECOGNITION SYSTEM USING DIRECTIONAL CODING AND BACKPROPAGATION NEURAL NETWORK"], "weko_shared_id": -1}