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The Optimization of Face Detection Technology Based on Neural Network and Deep Learning

The Optimization of Face Detection Technology Based on Neural Network and Deep Learning

Jian Zhao
Copyright: © 2023 |Volume: 16 |Issue: 3 |Pages: 14
ISSN: 1935-570X|EISSN: 1935-5718|EISBN13: 9781668489529|DOI: 10.4018/IJITSA.326051
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MLA

Zhao, Jian. "The Optimization of Face Detection Technology Based on Neural Network and Deep Learning." IJITSA vol.16, no.3 2023: pp.1-14. http://doi.org/10.4018/IJITSA.326051

APA

Zhao, J. (2023). The Optimization of Face Detection Technology Based on Neural Network and Deep Learning. International Journal of Information Technologies and Systems Approach (IJITSA), 16(3), 1-14. http://doi.org/10.4018/IJITSA.326051

Chicago

Zhao, Jian. "The Optimization of Face Detection Technology Based on Neural Network and Deep Learning," International Journal of Information Technologies and Systems Approach (IJITSA) 16, no.3: 1-14. http://doi.org/10.4018/IJITSA.326051

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Abstract

Face detection is a biometric technology that automatically contains facial feature information. It integrates digital image processing, pattern recognition, and other technologies and collects images or video streams containing human faces by cameras or cameras for automatic detection and tracking. Starting from the idea of local features and deep learning, aiming at the problem that traditional convolutional neural network (CNN) only extracts features from the whole image and ignores practical local details, this article proposes a deep CNN model based on the fusion of global and local features. It explores the face detection algorithm with better performance under the interference of illumination, expression, and other internal or external factors. This method designs a suitable network structure according to the size of the training data set, and the core technology is the debugging of super parameters. The simulation results show that compared with SVM, the improved CNN has obvious advantages in the later stage of operation, and the error is reduced by 36.85%. Compared with the traditional face detection method, it can automatically extract image features and also automatically learn its model and get a higher recognition rate.