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Identifier uch.csd.msc//2003delakis
Title Ανίχνευση Προσώπων Βασισμένη σε Συνελικτικά Νευρωνικά Δίκτυα
Alternative Title Face Detection Based on Convolutional Neural Networks
Creator Delakis, Manolis
Abstract Human face detection even though is performed instantly, effortlessly and with indicative accuracy by the human brain, for machine vision research is a matter still under development. Moreover, its wide range of applications like automatic data preparation for face recognition, content-based image retrieval or advanced human and computer interaction, make it a problem with both theoretical and practical values. The aim of this study was to introduce Convolutional Neural Networks as an efficient and fast face detector, able to operate in un-controlled image environments and without preprocessing. A convolutional neural topology is proposed, designed to be robust in varying image conditions and facial expression or other input deformations. The network was trained over a large enough training set of face patterns, coming directly from natural data, via the backpropagation algorithm. Using the trained convolutional filters of the network, a fast procedure for image scanning and face localization was devised, based purely on basic image processing operations. The system was tested in a series of large and difficult test sets exhibiting very high detection rates with a few and sporadic false alarms. The comparison with the current stateof- the-art systems in common benchmark sets revealed that the proposed system is the best performing general-purposed face detector of the reported literature. Furthermore, the tolerance of the network in a series of possible input deformations was measured and verified in conducted sensitivity analysis experiments.
Issue date 2003-07-01
Date available 2003-12-10
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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