APPLICATION OF FACENET MACHINE LEARNING MODEL AND HAAR CASCADE CLASSIFIER FOR BIOMETRIC IDENTIFICATION

Abstract

The paper analyzes the principles of Haar cascade classifier and FaceNET machine learning simulation program for biometric identification. As an experiment, a recognition system was created that will allow human face recognition, which was developed in the Python programming language. Some libraries covered in the research process include numpy, OpenCV, pip, matplotlib, virtualenv and pickle. The Haar cascade classifier is used to detect objects in images and videos. For the process of generating a facial signature, the two programs used the FaceNET machine learning model, which uses convolutional neural networks in the process of extracting features from facial images and conducting a comparative analysis of them between the identification data and those stored in the database. Due to this, FaceNET identifies faces in images with high accuracy and security, which is useful for use in access control systems, automation of visitor accounting processes and other applications where facial recognition is necessary. The created system will provide an opportunity to recognize unique facial features, store them and link them with the user's documentary information.