  This paper proposes a novel image set classification technique based on the
concept of linear regression. Unlike most other approaches, the proposed
technique does not involve any training or feature extraction. The gallery
image sets are represented as subspaces in a high dimensional space. Class
specific gallery subspaces are used to estimate regression models for each
image of the test image set. Images of the test set are then projected on the
gallery subspaces. Residuals, calculated using the Euclidean distance between
the 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 test
image set. We performed extensive evaluations of the proposed technique under
the challenges of low resolution, noise and less gallery data for the tasks of
surveillance, video-based face recognition and object recognition. Experiments
show that the proposed technique achieves a better classification accuracy and
a faster execution time compared to existing techniques especially under the
challenging conditions of low resolution and small gallery and test data.
