Once you have found the path to the directory where your Haar Cascades are stored, call CascadeClassifier and pass the path through it: xml files are, I suggest a quick file search for “haarcascade”. If you are having trouble finding the directory where these. For this tutorial, we’ll use an algorithm called the Haar Cascade for faces and eyes. xml files that contain facial recognition and other image processing algorithms. Once you’ve installed OpenCV, you should have access to. All image structures in OpenCV are can be converted to and from NumPy arrays so it may be useful to import NumPy as well. Begin by installing on your computer and then importing OpenCV (an open-source python package for image processing) into a py file. First, let’s find faces and eyes in a static picture. Now that we know the basics of how computer vision works, we can begin to build our filter. For facial recognition, the eyes are identified first (one of the most distinctive features found on a face), and then the rest of the features found on a face are mapped out using templates. Data scientists use machine learning algorithms to teach computers how to identify these and increasingly complex trends. Computers can imitate this line of thinking through trends in the pixel data. This process is called feature image extraction, where the user identifies features of the image to help them construct the overall image. The remaining pieces could really be anywhere. Edge pieces also have a distinct flat feature limiting the possible places it could go to only a set of possibilities along the edge. Puzzle pieces that are apart of the clover are easy to place because they have distinct shapes and can only logically go in one place. Image by Bill Selak Creative Commons License via Flickr Interestingly, computers “see” similarly to how humans solve jigsaw puzzles. Computer programmers can create algorithms that teach computers how to identify unique features in images. To a computer, images are just a series of numbers indicating where pixels are located. However, we can pretty easily create one ourselves using the OpenCV package in python, so we can use facial recognition anywhere. Curious about how to create some facial recognition tech yourself? Facebook offers the SparkAR platform to create facial recognition filters for Facebook and Instagram. From the utilitarian (unlocking your phone), to playful (Instagram filters), to the controversial (security, surveillance, and policing), our faces can be used by tech in many ways. Over the past 10 years, Facial recognition technology has developed rapidly and has quickly developed a variety of uses. Image by teguhjati pras Creative Commons License via Pixabay
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