Face Recognition Using Deep Learning as User Login on Healthcare Kiosk

This paper proposes the development of a login system to a Healthcare Kiosk using facial images. The use of the face as an example of a unique biometric system other than fingerprint and iris is considered better than conventional systems using RFID cards that are prone to being lost or left behind, or passwords that are often forgotten. In this paper, we propose the use of faces as input for the login system to a healthcare kiosk by utilizing deep learning technology. We tested four types of Convolutional Neural Network (CNN) architectures such as VGG16, ResNet50, Xception and MobileNet. In the accuracy testing process, VGG16 got a total accuracy of 100% but still showed the wrong class during realtime detection testing, ResNet50 got a total accuracy of 99.531% and was able to show the correct class during realtime detection testing, Xception got a total accuracy of 80.018% but still shows the wrong class when testing realtime detection, and MobileNet gets a total accuracy of 92.934%.