FACEMASK RECOGNITION IN PANDEMIC CAUSES MULTIPLE HEAD CRUCIAL PROBLEM CLASSIFICATION

Abstract

—A chronicle respiratory system condition like Breathing issues to Asthma patients after covering his/her mouth and nose make a tough challenge. Mainly the physical barrier are to take breath in oxygen, it also pack CO2 which the person exhale. A mask can feel high suffocation and add a compromised Head crucial problem stratification is in general used for the fore handling ahead of the facial acknowledgement & facial several inclination issues and only for this reason an algorithm like recognition of front facial expressions as a input images. However pretentious by Corona virus epidemic, public put on face masks to safe ourselves safety, for that face will protected by mask. However this research paper set up a proposed method in this research paper of combining the face portrait with the High speed - channel of the Hue Saturation Value color channel and grayscale image, and train the Convolution Neural Networks to enhance applications that is HGL method. In line portrait we have to generate the image insert it into Convolution Neural Networks for training. Without any processing insert the original picture of the Red Green Blue color space into the Convolution Neural Networks. Fine grained Net Structured Aggregation: FSA – NET stand for Fine grained Structured Aggregation. It is the method to use to remember the structure of a single Red Green Blue color space image. Since the output of this network is the Euler angle of the head pose, we have chosen a set of thresholds that are most effective for pose classification. The way to solve this issue is to provide help for the study of multi-angle problems. In practically, we can practice for a face detection algorithm that differentiate between wearing a mask or demask. If the facial image with masks, we can recommend this method proposed in this paper, and if it is a normal facial image, we can recommend Fine grained Structured Aggregation or Line Portrait algorithm, etc. The very first step of this algorithm to res