Face Mask Detection Using Machine Learning Project

Face mask detection is a significant project that is very suitable for open-ended global conditions, where the situation of wearing masks in public places is very efficient for public safety. The most efficient writers of phdprojects.org are PhD professionals we also know that only professionals can complete this task in correct time in a flawless manner and plagiarism free. Trending methodologies are user to get the correct result.

Here, we create a face mask detection system using machine learning by applying these guidelines:

  1. Objective Definition :
  • Considering the detection of an individual wearing a mask in a given image or real-time video, we must design a model for the identification process.
  1. Data Collection :
  • Dataset: We require datasets which consist of pictures of people with and without masks. An observance datasets are developed by us or public datasets are also available. If our own data is created, then check the differing backgrounds, mask types, lighting situations and camera angles.
  • Data Augmentation: Our datasets are extended by using tools like rotation, zoom, and horizontal flip for developing the model more powerful.
  1. Data Pre-processing :
  • Face Detection: If anyone wears a mask, we need to detect the face before the classification process. Pre-trained models or frameworks such as Haar cascades, MTCNN, or DLIB are employed in this method.
  • Image Resizing: The images are resized for attaining a similar size on the scale and it is relevant for machine learning models.
  • Normalization: Image pixels are standardized to the range of [0, 1] for gaining superior model performance.
  1. Model Selection and Training :
  • Classical Machine Learning: The attributes are derived from the images by using HOG, SIFT and we employ SVM (Support Vector Machine) and Random Forests.
  • Deep Learning Approach:
  • Make use of CNN-based structure and models such as MobileNet, ResNet, or custom architectures are getting trained from scratch or develop improvements.
  • The transfer learning is reviewed by applying our pre-trained models on Image Net and progressing the dataset for rapid merging and extreme accuracy.
  1. Evaluation :
  • Datasets are classified into training, validation and test sets.
  • Accuracy, precision, recall and F1-score are the metrics used by us for estimating the performance of a model.
  • Occupy the confusion matrix for getting awareness about false positives (type 1 error) and false negatives (type 2 error).
  1. Deployment :
  • Real-time Systems: We merge the model into a system that is associated with instantaneous video feeds, like surveillance cameras or entry-point cameras in public places.
  • Web or Mobile Application: An app is designed for users to upload their images or permits the camera for ensuring mask observations in real-time.
  1. Post-Deployment Monitoring and Updates :
  • For enhancing our model, retain fetching data post-deployment.
  • The model’s real world performance is observed by us and if it’s required, upgrade or retrain the model with improvements.


  • Variability in Masks: Our model must be vigorous for detecting the sufficient masks, because they are different from colors , patterns and sizes .
  • Partial Coverage: This mange the cases of the person who is wearing the mask inappropriately as it covers only the mouth or swinging on one year.
  • Occlusions: We handle the scenarios of the mask whether it is possibly concealed with a hand or scarf.

Future Enrichments:

  • Multi-class Classification: As an alternative of binary detection (mask/no mask), we establish a model for deciding whether the person wears the mask perfectly.
  • Integration with Temperature Detection: The public areas like airports or malls merge the system with thermal cameras for checking the temperature of individuals.

While working with all machine learning projects keep in mind that, interaction with field experts like health professionals which distribute us valuable perceptions and make certain model predictions that must line up with real-world demands and necessities.

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Face Mask Detection using Machine Learning Research Ideas

Face Mask Detection Using Machine Learning Project Thesis Ideas

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  1. An Improved Machine Learning Approach to Detect Real Time Face Mask


Deep Learning, Face detection, Support Vector Machine, Neural Network

An efficient face mask identification framework is suggested in our article by utilizing machine learning technique named Support Vector Machine (SVM). After the data creation, preprocessing of data is carried out in this study. Then the model is trained and examined. To identify the faces without a mask in an actual time, we can further construct the model and the identified faces are fed into neural network by using CNN to detect and punish them.

  1. Face Mask Detection Using Machine Learning


Computer Vision, OpenCV, Tensorflow, CNN Algorithm

To detect the persons who are not wearing masks in public places and notify the authorities is the major aim of our work. These are all carried out by employing AI techniques and image processing. A person with or without masks are detected by utilizing object recognition method. Here, IoT is responsible for transmitting notifications to the respective persons. Mask wearers and non-wearers are examined and noted in number.

  1. Real-Time Face Mask Detection using Computer Vision and Machine Learning


Covid 2 (SARS-CoV-2), SPP-NET, SSD, Faster R-CNN, Multi-Stage Object Detection

A main motive of face mask identification system is to detect the people who are not wearing mask by utilizing computer vision and ML approaches. In public transportation hub or in hospitals, this system can be utilized effectively to assure about the public health. The existence of face mask on people’s face is identified through the evaluation of videos or images obtained from cameras or computer visions technologies.

  1. Machine Learning based Real-Time Face Mask Detection System


Mask Detection, Keras, COVID-19, Real time automated systems, ML, Image Processing, Facial Recognition

In our research, through the evaluation of images, we can easily detect and identifies a mask wearable face. This framework is carried out by utilizing python machine learning packages such as tensor Flow, Keras, OpenCV. We are using two types of datasets for detection, one is related to wearing a mask and the other is related to without wearing a mask. This framework can assist to identify the masks on person’s face in an actual time.

  1. Face Mask Detection Using OpenCv and Machine Learning


Face Mask detection

We are developing an AI machine to detect whether there is a mask on people’s face or not are the main goal of this study. By doing so, it will assist people to keep the surroundings safe from the spreading out diseases. This work is accomplished by utilizing machine learning, deep learning and neural network. To build this framework efficiently, several tools such as jupyter notebook, numpy, opencv, tensorflow and learning tools are utilized.

  1. Face Mask Detection Using Machine Learning


Principle Component Analysis (PCA), RELU Accuracy Epochs

By employing various machine learning techniques such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Principal Component Analysis (PCA), we can identify the masks on person’s face by analyzing patterns in images. Images with persons wearing masks and without wearing masks are considers in this study. As a result, CNN outperforms other methods in detecting face masks.

  1. Face Mask Detection Using Machine Learning Techniques


Feature extraction, Image classification

To construct an innovative approach to identify the persons who are not wearing masks is the main aim of our research. Various CNN methods including Vgg16, MobileNetV2 and Densenet121 are utilized in our paper to categorize whether the person wearing face mask or not. All these methods are evaluated based on different performance metrics and all are efficient in forecasting about mask wearable and non-mask wearable persons.

  1. Real-Time Face Mask Detection Using Machine Learning Algorithm


Cascaded classifier (Haar cascade classifier), Accuracy, Loss score

Our recommended system includes two phases, that is development of framework by employing convolutional neural network (CNN) and the framework’s execution is carried out by utilizing cascaded classifier (Haar cascade classifier). By utilizing TensorFlow, Keras, and OpenCV, our static framework is developed. This system will assist the authorities to monitor security violations, recommend the utilization of mask and can maintain the environment safe.

  1. A novel machine learning scheme for face mask detection using pretrained convolutional neural network


Vgg16, Transfer learning, Image augmentation

By utilizing pretrained deep learning framework and Vgg16, our suggested model is developed to forecast whether the analyzed image wearing mask or not. In the Vgg16 model, only the last layer named fully connected layer is trained and it minimizes the training period and effort. As a consequence, model’s efficiency and accuracy is enhanced because of the utilization of vgg16 pretrained framework and augmentation of images. 

  1. Dynamic Face Mask Detection Using Machine Learning



By utilizing the face mask identification model, we can assist our surroundings by avoiding the spreading diseases. We can accomplish this model by employing various machine learning approaches such as TensorFlow, Keras, OpenCV, and Python. In a live stream, this model will monitor several faces at a time. This model can be enhanced by considering many features and in a crowded area, it would be very applicable.