The effectiveness of the Hermite wavelet discrete filter technique in modify a convolutional neural network for person identification

Classification is of great importance in the field of image processing, and convolutional neural networks (CNNs) have achieved great success in this field. Although CNN has proven to be a powerful technology for image recognition problems, it has failed in complex situations involving many realworld applications (for example, visual monitoring and automated driver assistance). Where it is difficult to detect a human in a series of images for various reasons. One of these reasons is the difference in the size of the human body, the height of the platform to which the camera is attached during the task of capturing accurate images, and the short training time in using the cameras, all of which are important factors to consider for the robustness and effectiveness of the human classification system. In this paper, a new deep CNN-based learning model is designed based on a new discrete waveform transformation (DWT) derived from discrete Hermit wavelet transform (DHWT) instead of modular wavelet, and the second stage is to train the convolutional neural network Hermit wavelets (HWCNN) is the most accurate and efficient deep learning.