Suranjana Samanta

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Face Recognition (FR) in surveillance scenarios has attracted the attention of researchers over the last few years. The bottleneck as a large gap in both resolution and contrast between training (high-resolution gallery) and testing (degraded, low quality probes) sets, must be overcome using efficient statistical learning methods. In this paper, we propose(More)
This paper describes a new method of unsupervised domain adaptation (DA) using the properties of the sub-spaces spanning the source and target domains, when projected along a path in the Grassmann manifold. Our proposed method uses both the geometrical and the statistical properties of the subspaces spanning the two domains to estimate a sequence of optimal(More)
Over the last few years, a few researchers have made attempts to bridge the gap between Training (high-resolution gallery) and Testing (degraded, low quality probes) sets for Face Recognition under the surveillance conditions, using efficient low-level processing and statistical learning methods. In this paper, this challenging task of FR in degraded(More)
This paper describes a method to learn Bag-Of-Words (BOW) descriptor for image representation which is robust to domain shift. Domain shift is necessary when a classifier trained on one dataset (source) is applied for classification on a different dataset (target). Datasets acquired with different conditions, have dissimilar feature distributions among(More)