• DocumentCode
    411603
  • Title

    Facial image database for law enforcement application: an implementation

  • Author

    Lai, Po-Jen ; Wang, Jan-Hwang

  • Author_Institution
    Inf. Center, The Cooperative Bank, Taipei, Taiwan
  • fYear
    2003
  • fDate
    14-16 Oct. 2003
  • Firstpage
    285
  • Lastpage
    289
  • Abstract
    This paper described an automatic facial feature extraction method from mug shots. Since most facial features locate at specific regions on a facial image, the region detection and partitioning techniques were used to segment and extract facial features. Heuristics were developed to detect the top, bottom, left and right margins of each feature region from the histograms of the vertical and horizontal projections of a facial image. Each facial feature region was then segmented according to its margins. Furthermore, each facial image was transformed to a facial feature vector, of which each element is the angle between two facial feature regions. The Euclidean distance was used to measure the similarities between facial feature vectors. A facial image database consists of three hundred mug shots was used for the experiment. The results show that the proposed scheme is computational efficient and performs well in facial image retrieval.
  • Keywords
    face recognition; feature extraction; image matching; image representation; image retrieval; image segmentation; law; visual databases; Euclidean distance; automatic facial feature extraction method; facial image database; facial image retrieval; law enforcement application; region detection; Data mining; Euclidean distance; Face detection; Facial features; Histograms; Humans; Image databases; Image retrieval; Image segmentation; Law enforcement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Security Technology, 2003. Proceedings. IEEE 37th Annual 2003 International Carnahan Conference on
  • Print_ISBN
    0-7803-7882-2
  • Type

    conf

  • DOI
    10.1109/CCST.2003.1297574
  • Filename
    1297574