{"id":17581,"date":"2023-06-09T18:31:00","date_gmt":"2023-06-09T13:01:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/real-time-face-detection\/"},"modified":"2024-11-11T13:37:15","modified_gmt":"2024-11-11T08:07:15","slug":"real-time-face-detection","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/real-time-face-detection\/","title":{"rendered":"Real-time Face detection | Face Mask Detection using OpenCV"},"content":{"rendered":"\n

In this article, we are going to find out how to detect faces in real-time using OpenCV<\/a>. After detecting the face from the webcam stream, we are going to save the frames containing the face. Later we will pass these frames (images) to our mask detector classifier to find out if the person is wearing a mask or not. <\/p>\n\n\n\n

We are also going to see how to make a custom mask detector using Tensorflow and Keras<\/a> but you can skip that as I will be attaching the trained model file below which you can download and use. Here is the list of subtopics we are going to cover:<\/p>\n\n\n\n

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  1. What is Face Detection?<\/a><\/li>\n\n\n\n
  2. Face Detection Methods<\/a><\/li>\n\n\n\n
  3. Face detection algorithm <\/a><\/li>\n\n\n\n
  4. Face recognition<\/a> <\/li>\n\n\n\n
  5. Face Detection using Python<\/a><\/li>\n\n\n\n
  6. Face Detection using OpenCV<\/a> <\/li>\n\n\n\n
  7. Create a model to recognise faces wearing a mask (Optional)<\/a><\/li>\n\n\n\n
  8. How to do Real-time Mask detection<\/a> <\/li>\n<\/ol>\n\n\n\n

    What<\/strong> is Face Detection?<\/strong><\/h2>\n\n\n\n

    The goal of face detection is to determine if there are any faces in the image or video. If multiple faces are present, each face is enclosed by a bounding box and thus we know the location of the faces<\/p>\n\n\n\n

    The primary objective of face detection algorithms is to accurately and efficiently determine the presence and position of faces in an image or video. The algorithms analyze the visual content of the data, searching for patterns and features that correspond to facial characteristics. By employing various techniques, such as machine learning, image processing, and pattern recognition, face detection algorithms aim to distinguish faces from other objects or background elements within the visual data, helping companies to enhance the cybersecurity<\/a> of their business<\/p>\n\n\n\n

    Human faces are difficult to model as there are many variables that can change for example facial expression, orientation, lighting conditions, and partial occlusions such as sunglasses, scarfs, masks, etc. The result of the detection gives the face location parameters and it could be required in various forms, for instance, a rectangle covering the central part of the face, eye centers or landmarks including eyes, nose and mouth corners, eyebrows, nostrils, etc.<\/p>\n\n\n\n

    Face Detection Methods<\/strong><\/h2>\n\n\n\n

    There are two main approaches for Face Detection:<\/p>\n\n\n\n

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    1. Feature Base Approach<\/li>\n\n\n\n
    2. Image Base Approach<\/li>\n<\/ol>\n\n\n\n

      Feature Base Approach<\/strong><\/h4>\n\n\n\n

      Objects are usually recognized by their unique features. There are many features in a human face, which can be recognized between a face and many other objects. It locates faces by extracting structural features like eyes, nose, mouth etc. and then uses them to detect a face. Typically, some sort of statistical classifier qualified then helpful to separate between facial and non-facial regions. In addition, human faces have particular textures which can be used to differentiate between a face and other objects. Moreover, the edge of features can help to detect the objects from the face. In the coming section, we will implement a feature-based approach by using the OpenCV tutorial<\/a>.<\/p>\n\n\n\n

      Image Base Approach<\/strong><\/h4>\n\n\n\n

      In general, Image-based methods rely on techniques from statistical analysis<\/a> and machine learning to find the relevant characteristics of face and non-face images. The learned characteristics are in the form of distribution models or discriminant functions that is consequently used for face detection. In this method, we use different algorithms such as Neural-networks, HMM, SVM<\/a>, AdaBoost learning<\/a>. In the coming section, we will see how we can detect faces with MTCNN or Multi-Task Cascaded Convolutional Neural Network<\/a>, which is an Image-based approach of face detection<\/p>\n\n\n\n

      Face detection algorithm<\/strong><\/h2>\n\n\n\n

      One of the popular algorithms that use a feature-based approach is the Viola-Jones algorithm<\/a> and here I am briefly going to discuss it. If you want to know about it in detail, I would suggest going through this article, Face Detection using Viola Jones Algorithm.<\/p>\n\n\n\n

      Viola-Jones<\/strong> algorithm is named after two computer vision researchers who proposed the method in 2001, Paul Viola<\/strong> and Michael Jones<\/strong> in their paper, \u201cRapid Object Detection using a Boosted Cascade of Simple Features\u201d. Despite being an outdated framework, Viola-Jones is quite powerful, and its application has proven to be exceptionally notable in real-time face detection. This algorithm is painfully slow to train but can detect faces in real-time with impressive speed.<\/p>\n\n\n\n

      Given an image(this algorithm works on grayscale images), the algorithm looks at many smaller subregions and tries to find a face by looking for specific features in each subregion. It needs to check many different positions and scales because an image can contain many faces of various sizes. Viola and Jones used Haar-like features to detect faces in this algorithm.<\/p>\n\n\n\n

      Face Recognition<\/strong><\/h2>\n\n\n\n

      Face detection and Face Recognition are often used interchangeably but these are quite different. In fact, Face detection is just part of Face Recognition.<\/p>\n\n\n\n

      Face recognition is a method of identifying or verifying the identity of an individual using their face. There are various algorithms that can do face recognition but their accuracy might vary. Here I am going to describe how we do face recognition using deep learning.<\/p>\n\n\n\n

      In fact here is an article, Face Recognition Python which shows how to implement Face Recognition.<\/p>\n\n\n\n

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