Jurusan Teknik Elektro
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Item Uncontrolled Environments Face Recognition based on Transfer Learning Technique for Secure Automatic Door Access System(Atlantis Press, 2024-12-25) Rinaldi, Anggi; Akbar, Andrianur, Ikram, Muhammad; Fahruzi, Iman; Fahruzi, ImanOver the past four decades, artificial intelligence technology, particularly in artificial neural networks and related methods, has advanced rapidly. Deep learning, a major branch of artificial intelligence, has proven its effectiveness in addressing various problems, especially those involving large-scale data such as images, text, and sound. One notable application of deep learning is in developing automated door systems. These systems offer several benefits, including reducing direct contact with door handles, which is increasingly important for cleanliness and health concerns. This research proposes using deep learning, specifically transfer learning techniques, to detect facial expressions of individuals approaching the door. By recognizing these facial expressions, the system can automatically activate a motor to open the door if the input matches the system’s criteria. During the development phase, we employed the MobileNetV2 architecture for facial expression detection. Testing was conducted with the ESP32 device, and the model was trained and validated over 25 epochs. The experiments revealed that the model achieved a maximum accuracy of 53%. This research contributes to creating more efficient and user-friendly automated door systems. By leveraging deep learning technology, we aim to enhance safety and comfort for users.Item Smart Access Control System Based On Uncontrolled Environment Human Face Recognition Using Convolutional Neural Network(ABEC Indonesia, 2025-01-16) Akbar, Muhammad Ikram Andrianur; Rinaldi, Anggi; Fahruzi, Iman; Fahruzi, ImanNeural networks or other artificial intelligence methods have developed rapidly over the past four decades. Its use in various fields makes many people compete to develop it further. One of the applications of artificial intelligence is an automatic door opening and closing system. This development can provide many advantages for users, one of which is that there is no need to make direct contact with the door handle. Armed with a capable PC and an esp32 microcontroller, the system works by detecting images of user facial expressions approaching the object using a webcam. If the required input matches the system rules, the motor will move to open the door. By using convolutional neural network technique, the system can classify the image quickly. Several expressions such as angry, disgusted, scared, happy, sad, surprised, and normal can be the door-opening key of the system. The user can select one to use as the input key to drive the motor to open the door. The study outcomes for several predetermined facial expressions yielded an accuracy rate of 60% and a detection time of under 4 seconds. The detectable distance extends to ± 2 meters. Further study could enable the development of this autonomous door with an IoT-based system for enhanced efficiency. Hopefully, this research can influence the development of intelligent building systems and other fields of artificial intelligence technology.