6/10/2023 0 Comments Face memory picturesFor the 2 face images of the same person, we tweak the neural network weights to make the vector closer via distance metric. The NN generates a 128-d vector for each of the 3 face images. Training the network is done using triplets:įigure 1: Facial recognition via deep metric learning involves a “triplet training step.” The triplet consists of 3 unique face images - 2 of the 3 are the same person. 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.įor 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. However, deep metric learning is different. And output a classification/label for that image.If you have any prior experience with deep learning you know that we typically train a network to: The secret is a technique called deep metric learning. So, how does deep learning + face recognition work? ![]() Understanding deep learning face recognition embeddings We’ll start with a brief discussion of how deep learning-based facial recognition works, including the concept of “deep metric learning.”įrom there, I will help you install the libraries you need to actually perform face recognition.įinally, we’ll implement face recognition for both still images and video streams.Īs we’ll discover, our face recognition implementation will be capable of running in real-time. Inside this tutorial, you will learn how to perform facial recognition using OpenCV, Python, and deep learning. Looking for the source code to this post? Jump Right To The Downloads Section Face recognition with OpenCV, Python, and deep learning
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