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<http://schema.org/description>
"This paper proposes a novel image set classification technique based on the\nconcept of linear regression. Unlike most other approaches, the proposed\ntechnique does not involve any training or feature extraction. The gallery\nimage sets are represented as subspaces in a high dimensional space. Class\nspecific gallery subspaces are used to estimate regression models for each\nimage of the test image set. Images of the test set are then projected on the\ngallery subspaces. Residuals, calculated using the Euclidean distance between\nthe original and the projected test images, are used as the distance metric.\nThree different strategies are devised to decide on the final class of the test\nimage set. We performed extensive evaluations of the proposed technique under\nthe challenges of low resolution, noise and less gallery data for the tasks of\nsurveillance, video based face recognition and object recognition. Experiments\nshow that the proposed technique achieves a better classification accuracy and\na faster execution time under the challenging testing conditions."
<http://api.deepai.org/publication/image-set-classification-for-low-resolution-surveillance>