Development of face and fingerprint recognition system in library environment

Management information systems have been adopted in various fields with the spread of computer technology. A library management system is an example of an information system. In recent years, computer-based systems containing artificial intelligence applications have been used for library information systems. Artificial neural networks are a qualitative method as a branch of artificial intelligence that can be used as a powerful tool to analyze complex information. In deep learning, a Convolutional Neural Network is a class of artificial neural networks most widely applied to analyze visual images. The aim of this thesis is to realize the face and fingerprint recognition system in the library environment by using Artificial Neural Networks and Convolutional Neural Networks. For this, Fingerprint datasets consist of 100 realistic fingerprints that are precisely classified into four fingerprint groups. Gray level formation matrix was used to extract features from fingerprint images. OpenCV provides various functions for computer vision applications. In this thesis, OpenCV' popular algorithms in OpenCV, which is used for face detection and face recognition, are used. Average and weiner filtering processes are used for the face recognition system. In this framework, FVC 2000, FVC 2002 and FVC 2004 data sets for finger recognition; For the face recognition system, experimental studies were carried out on the data set created in the system by realtime face recognition. As a result, it benefits real-time classification situations by speeding up the image processing of the classifier.