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dc.contributor.authorWang, Pingen_US
dc.contributor.authorLin, Wen-Huien_US
dc.contributor.authorChao, Kuo-Mingen_US
dc.contributor.authorLo, Chi-Chunen_US
dc.date.accessioned2018-08-21T05:57:08Z-
dc.date.available2018-08-21T05:57:08Z-
dc.date.issued2017-01-01en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ICEBE.2017.36en_US
dc.identifier.urihttp://hdl.handle.net/11536/147090-
dc.description.abstractNumerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous approaches including face recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the face recognition problem focus mainly on the static analysis to determine the face recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the face recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a face recognition process under the situation of vague facial features using deep reinforcement learning (DRL) approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of face recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of face recognition (100% at gamma correction gamma located in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting gamma = 1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.en_US
dc.language.isoen_USen_US
dc.subjectFace recognitionen_US
dc.subjecte-commerceen_US
dc.subjectDeep reinforcement learningen_US
dc.subjectConvolutional neuron networksen_US
dc.subjectBack propagationen_US
dc.titleA Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authenticationen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/ICEBE.2017.36en_US
dc.identifier.journal2017 IEEE 14TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE 2017)en_US
dc.citation.spage183en_US
dc.citation.epage188en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000426981100026en_US
Appears in Collections:Conferences Paper