Feb 8, 2014 · The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class.
To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.
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We show that a sparse illumination transfer (SIT) dictio- nary can be constructed to compensate the lack of the il- lumination information in the training set.
In the case of image corruption, since the corruption typically only affects a sparse set of pixel values, one can concurrently optimize a sparse error term in ...
The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for ...
Jul 2, 2014 · Furthermore, the face recogni- tion accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.
A novel algorithm to address single-sample face recognition based on a sparse representation based classification (SRC) framework is proposed, ...
Nov 21, 2014 · The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class.
To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.
Bibliographic details on Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment.