A survey on deep learning based face recognition

Materials for paper 'A survey on deep learning based face recognition, G Guo, N Zhang - Computer vision and image understanding, 2019.'

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This repository contains main papers/slides for the survey paper.

This survey presents a comprehensive overview of about 330 face recognition works using deep learning within the recent years
It shows that:

DL has been fully applied to FR and plays important roles;

Many specific issues or challenges have been addressed in FR by DL, e.g., pose, illumination, expression, 3D, heterogenous matching;

Various face datasets have been collected in recent years, including still images, videos, and heterogeneous data.

Deep learning, in particular the deep convolutional neural networks, has received increasing interests in face recognition recently, and a number of deep learning methods have been proposed.
This paper summarizes about 330 contributions in this area.
It reviews major deep learning concepts pertinent to face image analysis and face recognition, and provides a concise overview of studies on specific face recognition problems, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching.
A summary of databases used for deep face recognition is given as well.
Finally, some open challenges and directions are discussed for future research.

Face is the most common characteristic used by humans for recognition.
Face recognition (FR) is a classical problem and is still very active in computer vision and image understanding.
Fig. shows the pipeline of a typical automatic face recognition system.

A face image is fed into the system, and face detection and face alignment are processed. And then a feature extractor is used to extract features. Finally, the system compares the extracted features with the gallery faces to do face matching.

In face matching, there are two different tasks: face verification (FV) and face identification (FI).

FV is to determine whether a given pair of face images or videos belongs to the same subject.

FI is a one-to-many matching, recognizing the person from a set of gallery face images or videos of different subjects.