Aparna Bharati

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Human mobility patterns give insights into how people travel in their day-to-day lives. With availability of cellular data, either at large-scale but with low location accuracy or at small-scale but with high location accuracy, studying mobility patterns is now possible. An example of former dataset is CDRs (Call Detail Records) and that of latter is(More)
— Automatic face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques like convolutional neural networks (CNNs). But the training process for CNNs requires a large amount of clean and well-labelled training data. If a CNN is intended to work with non-frontal(More)
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS). Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos(More)
Digitally altering, or retouching, face images is a common practice for images on social media, photo sharing websites, and even identification cards when the standards are not strictly enforced. This research demonstrates the effect of digital alterations on the performance of automatic face recognition, and also introduces an algorithm to classify face(More)
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