In this video on OpenCV Python Tutorial For Beginners, we are going to see How we can do Face Recognition using Haar Feature based Cascade Classifiers. Using OpenCV and Python, you can recognize faces in minutes. Python Face Recognition in a Few Lines of Code
Code:
https://github.com/appydam/Real-time-Face-Recognition-Project
About Arpit:
LinkedIn – https://www.linkedin.com/in/arpitdhamija/ By the end of the tutorial, you will be able to build a lane-detection algorithm fuelled entirely by Computer Vision.
OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python.
it is Open Source and free. opencv is easy to use and install. Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV. At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science. Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems. resources:
https://docs.opencv.org/4.0.0/d7/d8b/tutorial_py_face_detection.html #ProgrammingKnowledge #ComputerVision #OpenCV
In this Python tutorial, you will learn how to perform face recognition using Python and the OpenCV library. Face recognition is a popular computer vision technique that involves identifying and verifying individuals based on their facial features. Here are the key topics covered in this tutorial: 1. Introduction to Face Recognition: Understand the basics of face recognition and its applications in various fields. 2. Installing OpenCV: Learn how to install the OpenCV library, which provides powerful tools for image and video processing. 3. Capturing and Preprocessing Images: Explore techniques for capturing images from a webcam or loading images from a dataset. Preprocess the images to enhance the quality and normalize the data. 4. Training the Face Recognition Model: Use a machine learning algorithm, such as the LBPH (Local Binary Patterns Histograms) or Eigenfaces algorithm, to train a face recognition model on a set of labeled images. 5. Detecting and Recognizing Faces: Implement face detection using OpenCV's Haar cascades or deep learning-based methods. Apply the trained face recognition model to recognize and identify individuals in real-time. 6. Handling Multiple Faces: Handle scenarios where multiple faces are detected in an image or video stream. Implement techniques to handle face tracking and identification for multiple individuals. 7. Improving Accuracy and Performance: Explore strategies to improve the accuracy and performance of the face recognition system, such as feature extraction, model optimization, and dataset augmentation. 8. Applications and Use Cases: Discover the practical applications of face recognition, including face authentication, attendance systems, surveillance, and more. By the end of this tutorial, you will have a solid understanding of how to perform face recognition in Python using the OpenCV library. You will be able to build your own face recognition applications and explore further advancements in this exciting field. Hashtags: #PythonTutorial #FaceRecognition #OpenCV #ComputerVision #MachineLearning #FacialRecognition #ImageProcessing #DeepLearning #PythonProgramming #ArtificialIntelligence #PythonProjects #ComputerVisionProjects #DataScience #PatternRecognition