3D FACE RECOGNITION MODEL TRAINING BASED ON 2D DEEP LEARNING ALGORITHM FOR BIOMETRIC IDENTIFICATION

• Main Author: Akbar Abhesa, Radifa
• Format: Final Project
• Language: Indonesia
• Subjects: Teknik (Rekayasa, enjinering dan kegiatan berkaitan)
• Online Access: https://digilib.itb.ac.id/gdl/view/48044
• Institution: Institut Teknologi Bandung

Biometric scanning system is a technology that used for identified someone into digital data based on biology uniqueness of a person. 2D facial recognition as a biometric identification system, nowadays is widely used because of it ease of use and highly accurate on identification process. Regardless of the benefits, there are still weakness on 2D facial recognition that are used with RGB camera while scanning can cause security problem. For that, we are proposed a facial recognition system design which made with 3D scanning system based on point cloud using stereo vision camera (kinect model 1414) along with facial detection algorithm that made from Python programming language with Deep learning method. Specifically, this report book explains the process of training the 3D Face Recognition model using a deep learning algorithm and evaluating it using the Confusion Matrix method. Deep learning that are obtained are models of training results using the Python ImageAI library with the YOLOv3 training model. Variations of the model tested in the form of numbers of different subjects and their impact on various parameters. The parameter testing is carried out in the form of measurement of accuracy, error, precision, and sensitivity of the model. The final result obtained are face recognition models with an accuracy range of 42.25 - 57%.