The age estimation of a face image can be posed as a deep classification problem using a CNN followed by an expected softmax value refinement (as can be done with a Deep EXpectation (DEX) model). In this recipe, you will first learn how to use a pre-trained deep learning model (a WideResNet with two classification layers added on top of it, which simultaneously estimates the age and gender using a single CNN) for age and gender recognition from a face image. We will use face images from the celebrity faces dataset for age and gender recognition. You will then implement emotion recognition using yet another pre-trained deep learning model, but this time you will need to detect the faces using a face detector (you could use transfer learning, too, and use your classifier on your own images, but this is left as an exercise...
Python Image Processing Cookbook
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Python Image Processing Cookbook
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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)
Preface
Image Manipulation and Transformation
Free Chapter
Image Enhancement
Image Restoration
Binary Image Processing
Image Registration
Image Segmentation
Image Classification
Object Detection in Images
Face Recognition, Image Captioning, and More
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