Materials for paper 'A survey on deep learning based face recognition, G Guo, N Zhang - Computer vision and image understanding, 2019.'
This repository contains main papers/slides for the survey paper.
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This survey presents a comprehensive overview of about 330 face recognition works using deep learning within the recent years
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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.
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Face is the most common characteristic used by humans for recognition.
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Face recognition (FR) is a classical problem and is still very active in computer vision and image understanding.
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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.
