Automatic Face Recognition

Dear Colleagues,

Automatic face recognition has a long history in both industry and academia. With the development of deep neural networks in the last years, tremendous improvements have been made, and nowadays automatic face recognition systems are applied, e.g., for border control in airports. While these systems work well in constraint environments with good illumination, high resolution images and cooperating subjects, face recognition in uncontrolled scenarios with unknown illumination, facial expression, face pose and/or occlusion still needs more research.

Closed-set recognition where only those subjects are tested that have been enrolled in the database gains high accuracies. On the other hand, automatic processing of surveillance camera images requires open-set recognition, where the recognition algorithm has to ignore unknown subjects not enrolled in the gallery and needs to deal with possible misdetections of the face detector. It has been shown lately that open-set face recognition is very far from being deployable.

This Special Issue targets researchers in the area of automatic face recognition, including 2D and 3D algorithms. While new research in open-set face recognition would be favorable, any of the following topics are welcome:

• Open-set face recognition
• Closed-set face recognition
• Face recognition in the wild
• Face recognition at a distance
• Face clustering
• Face detection
• Facial landmark localization
• Face recognition with convolutional neural networks
• 3D algorithms for face recognition
• Face recognition datasets and evaluation protocols

Since automatic face recognition has a long history in academia, there exists several small and outdated datasets. We recommend to use one of the novel datasets, such as IJB-B, and discourage submissions that rely, e.g., on the ORL dataset.

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• Face recognition
• Open-set
• Biometrics
• Deep neural networks
• Face detection
• Facial landmark localization