Abstract-Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the introduction of databases such as LFW and Pubfigs, face recognition community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various researchers contributing towards state-of-the-results. To further boost the unconstrained face recognition research, we introduce a more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs. The database consists of 34512 faces of 100 known actors collected from approximately 103 Indian movies. Unlike LFW and Pubfigs which used face detectors to automatically detect the faces from the web collection, faces in IMFDB are detected manually from all the movies. Manual selection of faces from movies resulted in high degree of variability (in scale, pose, expression, illumination, age, occlusion, makeup) which one could ever see in natural world. IMFDB is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related applications.
Social networks offer a wealth of information for capturing additional information on people's behavior, trends, opinions and emotions during any human-affecting events such as natural disasters. During disaster, social media provides a plethora of information which includes information about the nature of disaster, affected people's emotions and relief efforts. In this paper we propose a natural-disaster analysis interface that solely makes use of tweets generated by the Twitter users during the event of a natural disasters. We collect streaming tweets relating to disasters and build a sentiment classifier in order to categorize the users' emotions during disasters based on their various levels of distress. Various analysis techniques are applied on the collected tweets and the results are presented in the form of detailed graphical analysis which demonstrates users' emotions during a disaster, frequency distribution of various disasters and geographical distribution of disasters. We observe that our analysis of data from social media provides a viable, economical, uncensored and real-time alternative to traditional methods for disaster analysis and the perception of affected population towards a natural disaster.
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