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.
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Acknowledgments
The authors thank the North-Caucasus Federal University for supporting in the contest of projects competition of scientific groups and individual scientists of North-Caucasus Federal University.
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Kalita, D., Almamedov, P. (2023). Application of the SIFT Algorithm in the Architecture of a Convolutional Neural Network for Human Face Recognition. In: Alikhanov, A., Lyakhov, P., Samoylenko, I. (eds) Current Problems in Applied Mathematics and Computer Science and Systems. APAMCS 2022. Lecture Notes in Networks and Systems, vol 702. Springer, Cham. https://doi.org/10.1007/978-3-031-34127-4_35
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DOI: https://doi.org/10.1007/978-3-031-34127-4_35
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