Facial Age Estimation

Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the huge amount of human photos in the social networks. These images may provide no age label, but it is easily to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data via the deep Convolutional Neural Networks (CNNs). For each image pair, Kullback-Leibler divergence is employed to embed the age difference information(MS. SWATHI THILAKAN). The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. Experimental results on two aging face databases show the advantages of the proposed age difference learning system and the state-of-the-art performance is gained

Scheduling Sensors for Guaranteed Sparse Coverage

Sensor networks are particularly applicable to the tracking of objects in motion. For such applications, it may not necessary that the whole region be covered by sensors as long as the uncovered region is not too large. This notion has been formalized by Balasubramanian et.al. as the problem of $\kappa$-weak coverage. This model of coverage provides guarantees about the regions in which the objects may move undetected. In this paper, we analyse the theoretical aspects of the problem and provide guarantees about the lifetime achievable. We introduce a number of practical algorithms and analyse their significance. The main contribution is a novel linear programming based algorithm which provides near-optimal lifetime. Through extensive experimentation, we analyse the performance of these algorithms based on several parameters defined

Media exposure during infancy and early childhood: the effects of content and context on learning and development

Book Review: Media exposure during infancy and early childhood: the effects of content and context on learning and development, edited by Rachel Barr and Deborah Nichols Linebarger, Cham, Springer, 2017, 303 pp., $79.00 (ebook), ISBN 978-3-319-45102-2;$99.99 (hardcover), ISBN: 978-3-319-45100-

A Content Analysis of the Portrayal of India in Films Produced in the West

A content analysis of randomly selected films (N = 24) about India produced from 1930–2000 in the US or UK was undertaken to examine the social construction of “Indian-ness.” There were significant inter-group differences in depictions of Indian versus non-Indian scenes (N = 1016) and Indian versus non-Indian characters (N = 421) across several socio-cultural variables such as character role, occupation, poverty, religious practices, and pollution. Overall, India was consistently portrayed as backward, uncivilized, savage, and traditional. These patterns of stereotypical portrayals of India across films are discussed in the context of schema theory, social identity theory, and cultural colonization