  This work is unique in the use of discrete wavelets that were built from or
derived from Chebyshev polynomials of the second and third kind, filter the
Discrete Second Chebyshev Wavelets Transform (DSCWT), and derive two effective
filters. The Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT) is
used in the process of analyzing color images and removing noise and impurities
that accompany the image, as well as because of the large amount of data that
makes up the image as it is taken. These data are massive, making it difficult
to deal with each other during transmission. However to address this issue, the
image compression technique is used, with the image not losing information due
to the readings that were obtained, and the results were satisfactory. Mean
Square Error (MSE), Peak Signal Noise Ratio (PSNR), Bit Per Pixel (BPP), and
Compression Ratio (CR) Coronavirus is the initial treatment, while the
processing stage is done with network training for Convolutional Neural
Networks (CNN) with Discrete Second Chebeshev Wavelets Convolutional Neural
Network (DSCWCNN) and Discrete Third Chebeshev Wavelets Convolutional Neural
Network (DTCWCNN) to create an efficient algorithm for face recognition, and
the best results were achieved in accuracy and in the least amount of time. Two
samples of color images that were made or implemented were used. The proposed
theory was obtained with fast and good results; the results are evident shown
in the tables below.
