rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Cross Disciplinary Biometric Systems
  name:
    name part: Liu, Chengjun.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Mago, Vijay Kumar.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 228 p. online resource.
  abstract: Cross disciplinary biometric systems help boost the performance of the conventional systems. Not only is the recognition accuracy significantly improved, but also the robustness of the systems is greatly enhanced in the challenging environments, such as varying illumination conditions. By leveraging the cross disciplinary technologies, face recognition systems, fingerprint recognition systems, iris recognition systems, as well as image search systems all benefit in terms of recognition performance. Take face recognition for an example, which is not only the most natural way human beings recognize the identity of each other, but also the least privacy-intrusive means because people show their face publicly every day. Face recognition systems display superb performance when they capitalize on the innovative ideas across color science, mathematics, and computer science (e.g., pattern recognition, machine learning, and image processing). The novel ideas lead to the development of new color models and effective color features in color science; innovative features from wavelets and statistics, and new kernel methods and novel kernel models in mathematics; new discriminant analysis frameworks, novel similarity measures, and new image analysis methods, such as fusing multiple image features from frequency domain, spatial domain, and color domain in computer science; as well as system design, new strategies for system integration, and different fusion strategies, such as the feature level fusion, decision level fusion, and new fusion strategies with novel similarity measures.
  table of contents: Feature Local Binary Patterns -- New Color Features for Pattern Recognition -- Gabor-DCT Features with Application to Face Recognition -- Frequency and Color Fusion for Face Verification -- Mixture of Classifiers for Face Recognition Across Pose -- Wavelet Features for 3D Face Recognition -- Minutiae-based Fingerprint Matching -- Iris segmentation: state of the art and innovative methods -- Various Discriminatory Features for Eye Detection -- LBP and Color Descriptors for Image Classification.
  note: by Chengjun Liu, Vijay Kumar Mago.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Pattern recognition
  subject:
    topic: Biometrics (Biology)
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Biometrics
  subject:
    topic: Pattern Recognition
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Intelligent Systems Reference Library, 37
  identifier: 9783642284571
  identifier: http://dx.doi.org/10.1007/978-3-642-28457-1
  record info:
    record creation date: 120417
    record change date: 20151125221830.0
    record identifier: 978-3-642-28457-1