Application of the SIFT Algorithm in the Architecture of a Convolutional Neural Network for Human Face Recognition

Abstract

Solving the problem of pattern recognition is one of the areas of research in the field of digital video signal processing. Recognition of a person’s face in a real-time video data stream requires the use of advanced algorithms. Traditional recognition methods include neural network architectures for pattern recognition. To solve the problem of identifying singular points that characterize a person’s face, this paper proposes a neural network architecture that includes the method of scale-invariant feature transformation. Experimental modeling showed an increase in recognition accuracy and a decrease in the time required for training in comparison with the known neural network architecture. Software simulation showed reliable recognition of a person’s face at various angles of head rotation and overlapping of a person’s face. The results obtained can be effectively applied in various video surveillance, control and other systems that require recognition of a person’s face.