Computer Vision

• Verfasst von: Nelson, Abhilash [VerfasserIn]
• Titel: Computer Vision
• Titelzusatz: Face Recognition Quick Starter in Python
• Institutionen: Safari, an O’Reilly Media Company. [MitwirkendeR]
• Verf.angabe: Nelson, Abhilash
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Packt Publishing
• Jahr: 2020
• Umfang: 1 online resource (1 video file, approximately 3 hr., 51 min.)
• Fussnoten: Online resource; Title from title screen (viewed July 30, 2020)
• ISBN: 978-1-80056-722-1
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic videos ; local
• K10plus-PPN: 1733128964
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3755939959
• Lokale URL Inst.: Zum Volltext

Abstract

Build Python deep learning-based face detection, recognition, emotion, gender and age classification systems About This Video Use Python to detect and recognize faces from images and real-time webcam video Become well-versed with emotion detection Get up to speed with predicting age and gender from images and real-time webcam video In Detail Face detection and face recognition are the most popular aspects in computer vision. They are widely used by governments and organizations for surveillance and policing. Moreover, they also have applications in our day-to-day life such as face unlocking mobile phones. This course will help you delve into face recognition using Python without having to deal with all the complexities and mathematics associated with the deep learning process. You will start with an introduction to face detection and face recognition technology. After this, you’ll get the system ready for Python coding by downloading and installing the Anaconda package and other dependencies and libraries that are required such as dlib and OpenCV. You’ll then write Python code to detect faces from a given image and extract the faces as separate images. Next, you’ll focus on face detection by streaming a real-time video from the webcam. The course will also guide you on how to customize the face detection program to blur the detected faces dynamically from the webcam video stream. Moving ahead, you’ll go on to learn facial expression recognition and age and gender prediction using a pre-trained deep learning model. Later, you’ll progress to writing Python code for face recognition, which will help identify the faces that are already detected. You’ll use static images as well as live streaming video from the computer’s webcam to recognize the detected faces with their names. The course then explores the concept of face distance, teaching you how to convert the face distance value to face matching percentage using simple mathematics. Finally, you’ll be able to tweak the face landmark points used for face detection. You’ll draw a line joining the face landmark points to visualize the points in the face which the computer used for evaluation. Taking the landmark points customization to the next level, you’ll create custom face make-up for the face image. By the end of this course, you’ll be well-versed with face recognition and detection and be able to apply your skills in the real world.