International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 01 | Jan 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 887 Survey on Smart Entrance System with Machine Learning Technique Mugdha Patil 1 , Miheer Khambal 2 , Drashti Gosalia 3 , Akshay Dagare 4 , Mohan Kumar 5 1-4 BE Student, Electronics and Tele-Communication, Atharva College of Engineering, Mumbai, India 5 Professor, Electronics and Tele-Communication, Atharva College of Engineering, Mumbai, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - As a result of the outbreak of coronavirus (Covid- 19), human contact has become an important risk factor. It is widely believed that the risk of transmission of a virus increases with the greater number of people who come into contact with other objects or persons. World Health Organization (WHO) states that high fever and elevated heart rate are two of the most prevalent symptoms of covid19. Furthermore, it is recommended that we wash our hands frequently and use suitable masks .To detect mask from a person’s face a dataset of multiple with and without mask images are created Key Words: COVID19, Deep Learning, Open Source Computer Vision, Neural Network, mask detection, temperature 1. INTRODUCTION China was afflicted with a wide-spread outbreak of the new coronavirus (COVID-19) at the end of 2019 [1]. These viruses are a large family of different viruses causing a wide range of illnesses in humans. Some cause a common cold, others cause migraines, and others are linked to body ache. In the midst of this pandemic situation, everyone's health plays a vital role in their daily lives. However, the vast majority of the population is not aware of how to protect themselves and their surroundings from this threat [2]. Proper mask fitting, physical separation and hand hygiene are important for preventing the spread of the COVID-19 virus. Masks alone do not protect against the virus, and should be used along with regular hand wash and sanitization Due to the rapid spread of the (Covid-19) various countries are facing an epidemic of public health. For preventing covid,-19 many places have created an entrance system where people manually check a person's temperature, mask and sanitization is provided. This leads to social distance not being observed, also manual checkouts are not feasible in large crowds, and even sanitizing each person is neglected. So we are currently researching the possibility of developing an automatic detection system for facemasks and contact-less temperature checks that will provide individual protection. As such, at least this measure will allow the working population to leave the comfort of their homes to sustain their living, as well as help resolve the economic imbalance that has been brought about by Covid- 19. Ultimately, our research led to the development of a fully automated entrance system that consists of a contactless temperature scanner and a mask monitor. A human barrier is directly connected to the scanner. Entrance system is also equipped with an automatic contactless hand sanitizer as well. The Open Source Computer Vision (OpenCV) framework offers a pre-trained model for recognizing the faces. Using online pictures, the model was trained. The Raspberry Pi 3 receives the facemask data captured by the camera and processes it. This system will use Deep Learning and Computer Vision algorithms to detect individuals wearing a facemask on an image/video stream carried out using different libraries such as OpenCV, Keras, TensorFlow, and others. The photos are categorized as "mask" or "no mask" and obtained from several open source websites The MLX90614 sensor will now be used to measure the temperature [3]. The major objective of this research is to compare the performance of various classifiers and algorithms in terms of mask and temperature detection. This study, we hope, will aid researchers in their efforts to advance the field of contactless temperature sensing and mask detection approaches. The rest of the paper is laid out as follows. Section 2 discusses the literature on temperature sensing, contactless sanitization, and mask detection methods. Section 3 contains the discussion and conclusion. 2. LITERATURE REVIEW In [1] Li, Lixiang et. al. investigates study of the Corona Virus Disease 2019 transmission mechanism using official data modeling (COVID-19). Due to its exceptional spreading capacity and potential harm, the new coronavirus has posed a serious danger to people's health and safety all across the world. The study of local and worldwide epidemics, as well as the future development tendency, is a popular issue in contemporary research. Many teams are now researching the COVID-19 transmission legislation and prevention methods. The difference between the official data curve and the model is relatively minor. Simultaneously, it achieved forward prediction and backward inference of the pandemic scenario, with the appropriate analysis assisting relevant nations in making judgments. In [2] According to K. N. Baluprithviraj et.al. this project proposes an Artificial Intelligence (AI) based smart gadget (Raspberry Pi with AI model and camera) that detects if a person is wearing a face mask and sends us a warning message (via mobile app). A smartphone app is included with this gadget. When people are not physically present in their homes, a mobile app detects whether someone enters their house. This smart gadget only unlocks the door if visitors are wearing a face mask. This gadget may be used at any time of