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deep learning for face recognition in surveillance videos

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dc.contributor.advisor Dailey, Matthew
dc.contributor.author Sarmadi, Paul-Darius
dc.contributor.other Ekpanyapong, Mongkol
dc.contributor.other Parnichkun, Manukid
dc.date.accessioned 2016-05-11T07:08:34Z
dc.date.available 2016-05-11T07:08:34Z
dc.date.issued 2016-05-11
dc.identifier.other AIT
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/816
dc.description.abstract The problem of recognizing a previously identified criminal, hoping to follow him or her through video cameras feeds is a key issue. However, police require a great deal of human resources to perform this task. Automating the face verification process in surveillance video seems to be a feasible solution to this problem. Deep learning algorithms have recently reached particularly high levels of accuracy in automated face verification. For example, recent approaches reached over 98% accuracy on the Labeled Faces in the Wild (LFW) database. The goal of this research is to explore the possibility of adapting such methods to the particular conditions and constraints of video surveillance. I performed experiments on a database of surveillance videos acquired in a crowded shopping mall. With the neural network architecture described in this article, an accuracy of 88.04% was reached on the database. en_US
dc.publisher AIT en_US
dc.subject Data science en_US
dc.title deep learning for face recognition in surveillance videos en_US
dc.type Research report en_US

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