Adopting Haar Cascade Algorithm on Mask Detection System Based on Distance

COVID-19 which has hit almost the whole world, including Indonesia, which has become an epidemic in early 2020. Many cities and districts have enforced to comply with health protocols by using masks. All cities and regencies in South Sumatra are also required to follow health protocols by wearing masks and maintaining distance. So that the Mask Detection System program is a way to overcome public awareness, especially Bina Darma University that the importance of using masks today. In the case of making this mask detection system program using Python and using the Haar Cascade Algorithm. From experiments using the Haar Cascade method, the results show that this system can detect people who use masks and do not use masks. This test is also done by inputting images or videos. Futhermore, in testing this detection system, the approximate distance and angle also need to be considered because it will be very influential. © 2022 IEEE.

Deep Learning Based Intelligent Screening Mechanism

Facial recognition is widely used for identification of people as one of the biometric authentications. Biometric authentication consists of two types physiological and behavioral features. In physiological biometrics, faces, iris, and fingerprints are used for identifying the person. In behavioral biometrics, their characteristic features namely voice, DNA and hand writing is used. While using facial recognition, an individual can be identified using the previously trained model using deep learning based on the Haar cascade algorithm. Biometric authentication has been generally used for surveillance purposes. However, due to the COVID 19 pandemic, people of each nation are in need to wear face masks for their safety. Our project uses deep learning and open cv to recognize the person and to identify whether he wears a face mask or not by using transfer learning techniques and convolution neural network. One large dataset of people with mask and people without a mask was used as a training model. Our project was able to achieve an accuracy of 96.8% during the training and testing phase. © 2022 IEEE.

Automated Gate Control System using Face Mask Detection

This research paper gives a brief idea of controlling entrance gates of different areas like metro stations, railway stations, airports, corporate offices, restaurants, hotels and home with the face mask detection technology. In this, the camera will capture the real time video of a person using Artificial Intelligence[15],whosoever is entering the gate, processes the video and detects if the concerned person is wearing the mask properly or not. If the person is wearing a mask then the gate will open, if not then the gate will remain closed until the mask has been worn properly. The main motivation for this project comes from the current situation in the world where Covid-19 is spreading at a pace which is being difficult to control. This upcoming technology prototype can fuel in new ideas into different projects which are already ongoing to battle the pandemic. Also, the scope of this technology is not just limited to the face mask detection and has a wider and a more complex use-case in the real world. © 2022 IEEE.