Book Image

Python Image Processing Cookbook

By : Sandipan Dey
Book Image

Python Image Processing Cookbook

By: Sandipan Dey

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)

Face recognition using FaceNet

Face recognition is an image processing/computer vision task that tries to identify and verify a person based on an image of their face. Face recognition problems can be categorized into two different types:

  • Face verification (is this the claimed person?): This is a 1:1 matching problem (for example, a mobile phone that unlocks using a specific face uses face verification).
  • Face identification (who is this person?): This is a 1:K matching problem (for example, an employee entering an office can be identified by face identification).

FaceNet is a unified system for face recognition (for both verification and identification). It is sometimes called a Siamese network. It is based on learning a Euclidean embedding per image using a deep convolutional network that encodes an image of a face into a vector of 128 numbers. The network is trained (via a...