The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
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ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Terrascope Dataset: Scripted Multi-Camera Indoor Video Surveillance with Ground-truth
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
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Face hallucination and recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
Adaptive frame selection for improved face recognition in low-resolution videos
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Robust mean super-resolution for less cooperative NIR iris recognition at a distance and on the move
Proceedings of the 2010 Symposium on Information and Communication Technology
Evaluation of image resolution and super-resolution on face recognition performance
Journal of Visual Communication and Image Representation
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Computer Vision and Image Understanding
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Characteristics of surveillance video generally include low resolution and poor quality due to environmental, storage and processing limitations. It is extremely difficult for computers and human operators to identify individuals from these videos. To overcome this problem, super-resolution can be used in conjunction with an automated face recognition system to enhance the spatial resolution of video frames containing the subject and narrow down the number of manual verifications performed by the human operator by presenting a list of most likely candidates from the database. As the super-resolution reconstruction process is ill-posed, visual artifacts are often generated as a result. These artifacts can be visually distracting to humans and/or affect machine recognition algorithms. While it is intuitive that higher resolution should lead to improved recognition accuracy, the effects of superresolution and such artifacts on face recognition performance have not been systematically studied. This paper aims to address this gap while illustrating that super-resolution allows more accurate identification of individuals from low-resolution surveillance footage. The proposed optical flow-based super-resolution method is benchmarked against Baker et al.'s hallucination and Schultz et al.'s super-resolution techniques on images from the Terrascope and XM2VTS databases. Ground truth and interpolated images were also tested to provide a baseline for comparison. Results show that a suitable super-resolution system can improve the discriminability of surveillance video and enhance face recognition accuracy. The experiments also show that Schultz et al.'s method fails when dealing surveillance footage due to its assumption of rigid objects in the scene. The hallucination and optical flow-based methods performed comparably, with the optical flow-based method producing less visually distracting artifacts that interfered with human recognition.