• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Systematic evaluation of face detection algorithms on news videos

      Thumbnail
      View / Download
      726.6 Kb
      Author
      Acar, Can
      Atlas, Arda
      Çevik, Koray
      Ölmez İsa
      Ünlü, Mustafa
      Özkan, Derya
      Duygulu, Pınar
      Date
      2007
      Source Title
      2007 IEEE 15th Signal Processing and Communications Applications
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
      174
      views
      176
      downloads
      Abstract
      People are the most important subjects in news videos and for proper retrieval of people images; face detection is a very crucial step. However, face detection and recognition in news videos is a very challenging task due to the huge irregularities and high noise level in the data. In addition to that, with different face detection algorithms, the number and the type of the faces may differ. In this study, in order to get the best performance from existing methods, systematic evaluation of these methods is performed. In the experiments, news videos from TRECVID 2006 data set are used and for evaluation four different face detection methods are chosen.
      Keywords
      Challenging task
      Data sets
      Face Detection
      Face detection algorithms
      Face detection and recognition
      Face detection methods
      News videos
      Noise levels
      Systematic evaluation
      TRECVID
      Algorithms
      Signal detection
      Signal processing
      Face recognition
      Permalink
      http://hdl.handle.net/11693/27067
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2007.4298778
      Collections
      • Department of Computer Engineering 1398
      Show full item record

      Related items

      Showing items related by title, author, creator and subject.

      • Thumbnail

        A multi-modal video analysis approach for car park fire detection 

        Verstockt, S.; Hoecke, S. V.; Beji, T.; Merci, B.; Gouverneur, B.; Çetin, A. Enis; Potter, P. D.; Walle, R. V. D. (Elsevier, 2013)
        In this paper a novel multi-modal flame and smoke detector is proposed for the detection of fire in large open spaces such as car parks. The flame detector is based on the visual and amplitude image of a time-of-flight ...
      • Thumbnail

        Flame detection method in video using covariance descriptors 

        Habiboǧlu, Y.H.; Günay, Osman; Çetin, A. Enis (IEEE, 2011)
        Video fire detection system which uses a spatio-temporal covariance matrix of video data is proposed. This system divides the video into spatio-temporal blocks and computes covariance features extracted from these blocks ...
      • Thumbnail

        VOC gas leak detection using pyro-electric infrared sensors 

        Erden, Fatih; Soyer, E. B.; Toreyin, B. U.; Çetin, A. Enis (IEEE, 2010)
        In this paper, we propose a novel method for detecting and monitoring Volatile Organic Compounds (VOC) gas leaks by using a Pyro-electric (or Passive) Infrared (PIR) sensor whose spectral range intersects with the absorption ...

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy