Learn from Adam Geitgey and Davis King at PyImageConf 2018 The face_recognition library, created by Adam Geitgey, wraps around dlib’s facial recognition functionality, making it easier to work with. The dlib library, maintained by Davis King, contains our implementation of “deep metric learning” which is used to construct our face embeddings used for the actual recognition process.
In order to perform face recognition with Python and OpenCV we need to install two additional libraries: I would highly encourage you to read the above articles for more details on how deep learning facial embeddings work.
On the Labeled Faces in the Wild (LFW) dataset the network compares to other state-of-the-art methods, reaching 99.38% accuracy.īoth Davis King (the creator of dlib) and Adam Geitgey (the author of the face_recognition module we’ll be using shortly) have written detailed articles on how deep learning-based facial recognition works: The network itself was trained by Davis King on a dataset of ≈3 million images. Our network architecture for face recognition is based on ResNet-34 from the Deep Residual Learning for Image Recognition paper by He et al., but with fewer layers and the number of filters reduced by half. Our network quantifies the faces, constructing the 128-d embedding (quantification) for each.įrom there, the general idea is that we’ll tweak the weights of our neural network so that the 128-d measurements of the two Will Ferrel will be closer to each other and farther from the measurements for Chad Smith.
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