In today’s blog post you are going to learn how to perform face recognition in both images and video streams using: As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time. To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading!
Inside this tutorial, you will learn how to perform facial recognition using OpenCV, Python, and deep learning. We’ll start with a brief discussion of how deep learning-based facial recognition works, including the concept of “deep metric learning”.
From there, I will help you install the libraries you need to actually perform face recognition. Finally, we’ll implement face recognition for both still images and video streams. As we’ll discover, our face recognition implementation will be capable of running in real-time.
The secret is a technique called deep metric learning. If you have any prior experience with deep learning you know that we typically train a network to: However, deep metric learning is different.
Instead, of trying to output a single label (or even the coordinates/bounding box of objects in an image), we are instead outputting a real-valued feature vector. For the dlib facial recognition network, the output feature vector is 128-d (i.e., a list of 128 real-valued numbers) that is used to quantify the face. Read more from pyimagesearch.com…
thumbnail courtesy of pyimagesearch.com