Face Detection and Recognition Based on Raspberry Pi using HAAR Cascading and Convolution Neural Network

Abstract

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 paper, a face detection and recognition system were
proposed and developed for the purpose of 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 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.