Sparse representation based classification for face recognition by k-limaps algorithm
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Efficient misalignment-robust representation for real-time face recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Towards optimal design of time and color multiplexing codes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Robust and practical face recognition via structured sparsity
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Efficient point-to-subspace query in ℓ1 with application to robust face recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Digital paparazzi: spotting celebrities in professional photo libraries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Robust registration-based tracking by sparse representation with model update
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Shadow-Free TILT for facade rectification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Engineering Applications of Artificial Intelligence
Face recognition for web-scale datasets
Computer Vision and Image Understanding
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for public face data sets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.