Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Performance of optical flow techniques
International Journal of Computer Vision
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Face Detection for Visual Surveillance
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Video-Based Framework for Face Recognition in Video
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
ENCARA2: Real-time detection of multiple faces at different resolutions in video streams
Journal of Visual Communication and Image Representation
A trainable system for face detection in unconstrained environments
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Handbook of Biometrics
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Superresolution restoration of an image sequence: adaptive filtering approach
IEEE Transactions on Image Processing
Super-resolved faces for improved face recognition from surveillance video
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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Performing face detection and identification in low-resolution videos (e.g., surveillance videos) is a challenging task. The task entails extracting an unknown face image from the video and comparing it against identities in the gallery database. To facilitate biometric recognition in such videos, fusion techniques may be used to consolidate the facial information of an individual, available across successive low-resolution frames. For example, super-resolution schemes can be used to improve the spatial resolution of facial objects contained in these videos (image-level fusion). However, the output of the super-resolution routine can be significantly affected by large changes in facial pose in the constituent frames. To mitigate this concern, an adaptive frame selection technique is developed in this work. The proposed technique automatically disregards frames that can cause severe artifacts in the super-resolved output, by examining the optical flow matrices pertaining to successive frames. Experimental results demonstrate an improvement in the identification performance when the proposed technique is used to automatically select the input frames necessary for super-resolution. In addition, improvements in output image quality and computation time are observed. The paper also compares image-level fusion against score-level fusion where the low-resolution frames are first spatially interpolated and the simple sum rule is used to consolidate the match scores corresponding to the interpolated frames. On comparing the two fusion methods, it is observed that score-level fusion outperforms image-level fusion.