Application of AI techniques for Face Mask Detection

Covid-19 is a completely new problem, and we have seen it move to a brand new level. After the 3rd wave of Covid-19 in India and predictions of another wave this year it is a major concern and still many people are not following basic precautionary measures like wearing a mask in public locations this can be solved by our face mask detection program we want to be short a good way to respond to new facts, which they are all around us. Growing a secure environment can be paramount the human to make lifestyles as smooth as ever. Alternatives have to be taken to protect all who go back and to maintain them our loved ones who have no troubles. New era packages are being made each day to satisfy regulations and regulations but, the face mask becomes a new well known used for regular existence, but, to create a more secure surroundings that contributes to public protection, a want to be diagnosed at some stage in date and motion towards people who do not put on masks in public locations or offices. Many sections of the general public appear to simply accept Covid adherence protection gear. A face masks detector is among the most crucial equipment. This software allows one to find out who does not have the desired face masks. Those applications with them current tracking systems and neural network algorithm to see if an individual has put on a mask or not. About this, we'll do discussion in short the synthetic intelligence and its small additives specifically device gaining knowledge of and in-intensity analysing, in-intensity reading frameworks followed with the aid of the usage of simplicity implementation of face masks detection machine. © 2022 IEEE.

A novel DCNN-ELM hybrid framework for face mask detection

The Coronavirus disease (2019) has caused massive destruction of human lives and capital around the world. The latest variant Omicron is proved to be the most infectious of all its previous counterparts – Alpha, Beta and Delta. Various measures are identified, tested and implemented to minimize the attack on humans. Face masks are one of those measures that are shown to be very effective in containing the infection. However, it requires continuous monitoring for law enforcement. In the present manuscript, a detailed research investigation using different ablation studies is carried out to develop the framework for face mask recognition using pre-trained deep convolution neural networks (DCNN) models used in conjunction with a fast single layer feed-forward neural network (SLFNN) commonly known as Extreme Learning Machine (ELM) as classification technique. The ELM is well known for its real time data processing capabilities and has been successfully applied both for regression and classification problems of image processing and biomedical domain. It is for the first time that in this paper we have proposed the use of ELM as classifier for face mask detection. As a precursor to this, for feature selection, six pre-trained DCNNs such as Xception, Vgg16, Vgg19, ResNet50, ResNet 101 and ResNet152 are tested for this purpose. The best testing accuracy is obtained in case of ResNet152 transfer learning model used with ELM as the classifier. The performance evaluation through different ablation studies on testing accuracy explicitly proves that ResNet152 - ELM hybrid architecture is not only the best among the selected transfer learning models but also proves so when it is compared with several other classifiers used for the face mask detection operation. Through this investigation, novelty of the use of ResNet152 + ELM for face mask detection framework in real time domain is established. © 2022

A Novel DCNN-ELM Hybrid Framework For Face Mask Detection

The Coronavirus disease (2019) has caused massive destruction of human lives and capital around the world. The latest variant Omicron is proved to be the most infectious of all its previous counterparts – Alpha, Beta and Delta. Various measures are identified, tested and implemented to minimize the attack on humans. Face masks are one of those measures that are shown to be very effective in containing the infection. However, it requires continuous monitoring for law enforcement. In the present manuscript, a detailed research investigation using different ablation studies is carried out to develop the framework for face mask recognition using pre-trained deep convolution neural networks (DCNN) models used in conjunction with a fast single layer feed-forward neural network (SLFNN) commonly known as Extreme Learning Machine (ELM) as classification technique. The ELM is well known for its real time data processing capabilities and has been successfully applied both for regression and classification problems of image processing and biomedical domain. It is for the first time that in this paper we have proposed the use of ELM as classifier for face mask detection. As a precursor to this, for feature selection, six pre-trained DCNNs such as Xception, Vgg16, Vgg19, ResNet50, ResNet 101 and ResNet152 are tested for this purpose. The best testing accuracy is obtained in case of ResNet152 transfer learning model used with ELM as the classifier. The performance evaluation through different ablation studies on testing accuracy explicitly proves that ResNet152 - ELM hybrid architecture is not only the best among the selected transfer learning models but also proves so when it is compared with several other classifiers used for the face mask detection operation. Through this investigation, novelty of the use of ResNet152 + ELM for face mask detection framework in real time domain is established.

Covid Prevention System for Face Mask Detection and Temperature Sensing

Currently, the world is experiencing a serious medical crisis as a result of the Corona virus COVID-19, which now has swept the globe. For several countries, combating this disease outbreak has become an unfortunate reality. Wearing a face mask when going outside or meeting with others is essential for prevention. Some irresponsible people, on the other hand, refuse to wear face masks for a variety of reasons. The development of the face mask detector too is critical in this case. To address this problem, a reliable face mask detector must be created. A face mask can be detected using the object detection algorithm. The mask detection algorithm used to detect the face mask was Haar Cascade in OpenCV from Python. According to the results of the experiments, this device can detect whether or not someone is wearing a face mask and can also measure body temperature. Once these validations are completed automatically door gets opened and sanitization is done. © 2022 IEEE.

Speech2Emotion: Intensifying Emotion Detection Using MLP through RAVDESS Dataset

Human feeling recognition, regardless of whether through the face or through voice, is as yet a generally new field of study. Discourse Emotion recognition is the assignment of recognizing a speaker's feelings from their discourse records. Seeing sentiments from discussion can assist with deciding a person's physical and passionate prosperity. These sentiments can be used to survey the consumer loyalty in their own normal language. For this, this exploration tries to sort feelings in four distinct structures like Happy, Neutral, Sad and Angry. SVM, MLP, RF and Decision Tree calculations are utilized for investigation. RAVDESS information base is taken for arrangement. To identify feelings, qualities, for example, mfcc, chroma, tomez differentiation, and mel were recovered and taken care of into the model. MLP gives the bestoutcome with 85% exactness when contrast and others. © 2022 IEEE.

Face Mask Detection Using Convolutional Neural Network

COVID-19 is one of the most dangerous forms of diseases which is caused by corona virus. It is a highly transmissible disease. WHO has already declared it as a pandemic.Therefore at the current scenario, due to outbreak of this pandemic (COVID-19), face masks has become the necessary tool of everyone to avoid spread of disease to some extent. There has been a great demand for the development of software which can easily recognize person who is wearing a mask. Therefore we are going to develop a system which will fulfill the need using Deep Learning. We will use convolutional neural network to train our model. Here two categories of dataset will be used. The one which contains set of images of faces with mask and the other without face masks We will train the program with this data set to learn to decide whether a person's face is masked or not. OpenCV, tensorflow and keras will be used for the real time face detection with live stream through web camera. After the successful deployment of the product, we will be able to design a software which can be installed at various places such as at the entry gate of colleges, railway stations , air ports , temples, hotels and shops etc. This will easily detect the persons who are entering without face masks by using cameras of the systems. Hence this product is the need of the hour for us to develop so as to work for the safety of humans. © 2021 IEEE.

FaceMask: A New Image Dataset for the Automated Identification of People Wearing Masks in the Wild.

The rapid spread of the COVID-19 pandemic, in early 2020, has radically changed the lives of people. In our daily routine, the use of a face (surgical) mask is necessary, especially in public places, to prevent the spread of this disease. Furthermore, in crowded indoor areas, the automated recognition of people wearing a mask is a requisite for the assurance of public health. In this direction, image processing techniques, in combination with deep learning, provide effective ways to deal with this problem. However, it is a common phenomenon that well-established datasets containing images of people wearing masks are not publicly available. To overcome this obstacle and to assist the research progress in this field, we present a publicly available annotated image database containing images of people with and without a mask on their faces, in different environments and situations. Moreover, we tested the performance of deep learning detectors in images and videos on this dataset. The training and the evaluation were performed on different versions of the YOLO network using Darknet, which is a state-of-the-art real-time object detection system. Finally, different experiments and evaluations were carried out for each version of YOLO, and the results for each detector are presented.