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Vol.12, No.2, May 2023. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Implementation of Face Recognition for Patient Identification Using the Transfer Learning Method
Stephanie Pamela Adithama, Martinus Maslim, Rio Gunawan
© 2023 Martinus Maslim, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 12, Issue 2, Pages 775-784, ISSN 2217-8309, DOI: 10.18421/TEM122-22, May 2023.
Received: 18 January 2023. Revised: 12 April 2023.
Abstract:
The hospital's status as a health center requires it to ensure patient safety, decrease incidents and treat patients. Identification of the patient is the primary source of patient safety difficulties. In addition to the patient's name and number, further patient-identifying components are needed to reduce this neglect. This work provides a solution in the form of biometric authentication, namely, face recognition. The convolutional neural network (CNN) approach can enable machine facial recognition. CNN is one of the deep learning techniques used to detect and identify picture objects. In this study, facial recognition was carried out using the transfer learning technique, VGGFace2 model pretraining, and SENet 50 model architecture. The dataset was collected via one-shot learning or a single sample per individual sampling. Applying the CNN model to the patient identification system yields two distinct outcomes: patient registration and verification. Registration utilizes a minimum distance of 0.35 and matches data with the complete database, whereas patient verification has a minimum distance of 0.28 and matches only the face in question. At the time of patient registration, the accuracy was between 90% and 100%. However, at the time of patient verification, the accuracy was 100%.
Keywords –patient identification, face recognition, convolutional neural network (CNN), SENet 50, VGGFace2. |
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