Скачать или смотреть How Face Recognition Works with Deep Learning in Python

This is a hands-on face recognition workshop from scratch in python. In this episode, we are going to mention how face recognition works within deep learning and common stages of a modern face recognition pipeline.

Modern face recognition pipelines consist of 4 common stages: detect, align, represent and verify.

The both detection and alignment can be handled by common packages such as opencv or dlib. Those packages use adaboost algorithm in the background. It is a legacy boosted tree.

We feed face images to a convolutional neural network and extract its vector embedding in representation step. Herein, the most common face recognition models are VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, ArcFace, DeepID and Dlib. These models were built to classify face pictures on a large scale data set. We use its early layers to represent.

Then, verification step finds distances between two vectors found in the representation step. Herein, euclidean distance and cosine similarity are the most common methods to compare vectors. Pictures of a same person should have a small distance whereas pictures of different person should have a large distance.

We will use pre-built face recognition models provided by deepface framework for python. You can install deepface with calling "pip install deepface" command.

Tutorial: https://sefiks.com/2020/05/01/a-gentl...

Repository: https://github.com/serengil/deepface

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