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.

本文构建了一个基于无线传感器的人脸识别和运动训练动作的低分辨率模型。该模型通过计算图像熵值得到人脸图像中信息大小的分布，根据信息大小分配不同的权重进行人脸识别计算，使得原来基于模块的算法只是简单地基于图像基于熵的分割。将值的大小分成块，解决了类别信息的计算量化问题。在测试阶段，采用传统的正交匹配追踪算法求解编码系数，通过计算人脸图像的类内矩阵和运动训练动作图像的类间矩阵，得到了很好的分类识别结果。在分类问题上表现良好的方法进一步提高了人脸识别率。具体处理过程是在输入人脸图像中加入高斯噪声、椒盐噪声，并减小输入图像中人脸图像的尺寸，从而使改进算法得到改进。实验结果表明，采用高效分辨率传感技术学习两种模态对应的运动训练动作，得到的高分辨率模态图像与低分辨率模态图像之间的矩阵系数达到0.971，迭代率高提高了 71.5%，