000 03798nam a22005055i 4500
001 978-3-642-28457-1
003 DE-He213
005 20140220083312.0
007 cr nn 008mamaa
008 120417s2012 gw | s |||| 0|eng d
020 _a9783642284571
_9978-3-642-28457-1
024 7 _a10.1007/978-3-642-28457-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aLiu, Chengjun.
_eauthor.
245 1 0 _aCross Disciplinary Biometric Systems
_h[electronic resource] /
_cby Chengjun Liu, Vijay Kumar Mago.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2012.
300 _aXVI, 228p. 112 illus., 58 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v37
505 0 _aFeature 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.
520 _aCross 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 0 _aBiometrics.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aBiometrics.
650 2 4 _aPattern Recognition.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aMago, Vijay Kumar.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642284564
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v37
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-28457-1
912 _aZDB-2-ENG
999 _c102813
_d102813