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This paper proposes a novel image set classification technique based on theconcept of linear regression. Unlike most other approaches, the proposedtechnique does not involve any training or feature extraction. The galleryimage sets are represented as subspaces in a high dimensional space. Classspecific gallery subspaces are used to estimate regression models for eachimage of the test image set. Images of the test set are then projected on thegallery subspaces. Residuals, calculated using the Euclidean distance betweenthe original and the projected test images, are used as the distance metric.Three different strategies are devised to decide on the final class of the testimage set. We performed extensive evaluations of the proposed technique underthe challenges of low resolution, noise and less gallery data for the tasks ofsurveillance, video-based face recognition and object recognition. Experimentsshow that the proposed technique achieves a better classification accuracy anda faster execution time compared to existing techniques especially under thechallenging conditions of low resolution and small gallery and test data.

Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach