Low-Resolution Face Recognition and Sports Training Action Analysis Based on Wireless Sensors

• Published in: Journal of circuits, systems, and computers Vol. 32; no. 2
• Main Authors: An, Hongjun, Gao, Heng
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 30.01.2023; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Entropy; Face recognition; Image classification; Image resolution; Image segmentation; Iterative methods; Matched pursuit; Object recognition; Random noise; Sensors; Sports; Sports training; Training; face recognition; low resolution image; Wireless sensor; sports training analysis
• ISSN: 0218-1266; 1793-6454
• DOI: 10.1142/S0218126623500378

This paper constructs a low-resolution model for face recognition and sports training actions based on wireless sensors. The model obtains the distribution of the information size in the face image by calculating the image entropy value, and assigns different weights according to the size of the information to perform face recognition calculation, so that the original module-based algorithm is simply based on image segmentation into one based on entropy. The size of the value is divided into blocks, which solves the problem of computational quantification of category information. In the test stage, the traditional orthogonal matching pursuit algorithm is used to solve the coding coefficients, and the excellent classification and recognition results are obtained by calculating the intra-class matrix of the face image and the inter-class matrix of the sports training action image. Methods that perform well on classification problems further improve face recognition rates. The specific processing process is to add Gaussian noise, salt and pepper noise to the input face image and reduce the size of the face image in the input image, so that the improved algorithms are improved. The experimental results show that the high-efficiency resolution sensing technology is used to learn the sports training actions corresponding to the two modalities, and the matrix coefficient between the obtained high-resolution modal and low-resolution modal images reaches 0.971, and the iteration rate is improved by 71.5%, effectively promoting the high recognition rate of faces and actions.