Face detection and recognition based on raspberry Pi using HAAR cascading and convolution neural network

Face Detection is a form of biometric method that relates to the automatic detection of faces by computerized systems through observation of the face. It is a popular feature in biometrics, digital cameras, and social tagging. Face detection and recognition have received increased research focus in recent years. In this thesis, a face detection and recognition system have been proposed and developed for detecting and recognition faces through the hybridization of two algorithms: HAAR cascading algorithm and deep learning algorithm. The proposed system consists of two approaches. The first approach, the HAAR cascading algorithm, was developed by taking a shot of the face and reducing it several times to ensure that there is a face at each shrinking time. The second approach has proposed convolution neural network (CNN) model to increase accuracy of classification. In addition to improving each algorithm, hybridization of the two algorithms significantly improved the results of the classification. In proposed system two dataset was used: download dataset, and real dataset. The accuracy of modifying HAAR in detection reached 98.667% for real dataset, and 97.532 % for download dataset. The accuracy of proposed model of CNN in classification reached 96.23% for download dataset, and 100% for real dataset. The tests conducted on the developed facial recognition system have demonstrated that the proposed algorithms and the developed real-time facial recognition system yield satisfactory results. Key Words: Face Detection, Face Recognition, HAAR Cascading, CNN.