DEVELOPMENT OF FACE RECOGNITION SYSTEM USING OBJECT DETECTION MODEL YOLO AND FACENET

• Main Author: Pandita Prayogo, Bagas
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/74076
• Institution: Institut Teknologi Bandung

Advances in information technology encourage digitization of all aspects of human life, including government. The Indonesian government also uses the Electronic Based Government System (SPBE) to transform governance by utilizing information and communication technology to provide services to SPBE users. However, with this transformation, some weaknesses and loopholes can be exploited by irresponsible people, such as attacks on systems, theft of identity and personal data, and falsifying electronic documents. Therefore, a method is needed to verify the user's identity. The authors developed a facial recognition system that uses faces to identify and verify a person's identity to meet this need. The author develops a face recognition system by utilizing an existing pre-trained model, namely the YOLO (You Only Look Once) deep learning model, as a face detection system with various weights and different versions. Furthermore, the face detection system will be integrated with Facenet to perform feature extraction and face classification. The system will be tested and rated based on the average time it takes to process images and their accuracy. After testing a dataset consisting of 100 identities and 200 student facial images with a device that has an AMD Ryzen 4600H CPU and 16 GB RAM, the best results obtained are the YOLOv5 model and yolov5s weights with a processing time of 183 ms, 99.5 % of accuracy and 100% of precision.