Real-Time Anti Spoofing Face Detection with Mask Using CNN

As COVID-19 spread the whole way across the world, a significant number of us got mindful of how significant face covers are. Medical services authorities and nearby foundations from one side of the planet to the other are encouraging individuals to wear masks ,as it is the best way to forestall the transmission of the infection. Masks have without a doubt frustrated the facial-acknowledgment industry; the innovation has likewise adjusted. It might sound odd yet wearing a cover does not really prevent a PC from recognizing somebody. We are intending to prepare our model to recognize whether the pictures are genuine or fake one even though individuals are wearing face cover. In this paper, we intend to make a liveness detector equipped for spotting counterfeit faces. To make a liveness detector, we will prepare a deep learning neural network fit for recognizing genuine versus counterfeit appearances. It deals with two correlative spaces: RGB space and multi-scale Retinex (MSR) space. The RGB space contains the point-by-point facial surfaces, yet it is sensitive to illumination whereas the MSR pictures can adequately catch the high recurrence data, which is discriminative for face recognition

A Review of Image Super Resolution using Deep Learning

The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designed, image enhancement used or not, among other factors. To improve the accuracy of the image super resolution, a different deep learning techniques might be explored. In fact, greater research into the image super resolution in medical imaging is possible to improve the data's suitability for future analysis. In light of this, it can be said that there is a lot of scope for research in the field of medical imaging