Suranjana Samanta

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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)
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)
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)
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)
Domain adaptation (DA) is the process in which labeled training samples available from one domain is used to improve the performance of statistical tasks performed on test samples drawn from a different domain. The domain from which the training samples are obtained is termed as the source domain, and the counterpart consisting of the test samples is termed(More)