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2009 | nr 3 (20) | 103-112
Tytuł artykułu

Two-dimensional canonical correlation analysis for face image processing and recognition

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Paper presents the method of two-dimensional canonical correlation analysis (2DCCA) as applied to image processing and biometrics. Method is based on representing the image as the sets of its rows (r) and columns (c) and implementation of CCA using these sets (for these reason we named the method as CCArc). CCArc features simple implementation and lesser complexity than other known approaches. In applications to biometrics CCArc is suitable to solving the problems when dimension of images (dimension of feature space) is greater than number of images, i.e. Small Sample Size problem (SSS). Demonstrated high efficiency of CCArc method for a number of computer experiments. Experiments itself are described with compact notation allowing to use its results in the framework of meta-analysis.
Wydawca

Rocznik
Tom
Strony
103-112
Opis fizyczny
Bibliogr. 13 poz., rys.
Twórcy
autor
  • West Pomeranian University of Technology, Szczecin, Poland, Faculty of Computer Science and Information Technology
Bibliografia
  • [1] Hotelling H. Relations between two sets of variates. Biometrika 28, 1936, pp. 321–377.
  • [2] Donner R., Reiter M., Langs G., Peloschek P.., and Bischof H. Fast Active Appearance Model Search Using Canonical Correlation Analysis. IEEE Transaction on PAMI, Vol.28, No. 10, October 2006, pp. 1960 – 1964.
  • [3] Dong Yi, Rong Liu, RuFeng Chu, Zhen Lei and Stan Z. Li, Face Matching Between Near Infrared and Visible Light Images, Lecture Notes in Computer Science, Volume 4642, 2007, pp. 523-530.
  • [4] Shan C., Gong S., McOwan P. W. Fusing gait and face cues for human gender recognition. Neurocomputing No. 71, 2008, 1931– 1938
  • [5] Szaber M., Kamenskaya E. Systemy rozpoznawania twarzy dla obrazów widzialnych i podczerwieni z wykorzystaniem CCA. Metody Informatyki Stosowanej, No. 3/2008, Vol. 16, 2008, pp. 223-236.
  • [6] 10. ISO/IEC JTC 1/SC 37 N 506: Biometric Data Interchange Formats, Part 5: Face Image Data – www.icao.int/mrtd/download/technical.cfm
  • [7] Magnus Borga. Canonical Correlation a Tutorial. January 12, 2001. http://www.imt.liu.se/~magnus/cca/tutorial/tutorial.pdf.
  • [8] Lee Sun Ho and Choi Seungjin. Two-Dimensional CCA. IEEE Signal Processing Letters, Vol. 14, No. 10, October 2007, pp. 735 – 738.
  • [9] Zou Cai-rong, Sun Ning, Ji Zhen-hai, Zhao Li. 2DCCA: A Novel Method for Small Sample Size Face Recognition. IEEE Workshop on Application of Computer Vision, WACV’07, 2007, pp. 43-47.
  • [10] Kukharev G., Forczmanski P. Facial Images Dimensionality Reduction and Recognition by Means of 2DKLT. Machine Graphics & Vision, Vol.16, No. 3/4, 2007, pp. 401-425.
  • [11] Кухарев Г.А., Щеголева Н.Л. Системы распознавания человека по изображению лица. (Face Recognition Systems) Монография: Из-во СПбГЭТУ (ЛЭТИ), СПб, 2006, 176 с.
  • [12] Кухарев Г.А. Поиск изображений лиц в больших базах данных. Журнал «МИР ИЗМЕРЕНИЙ» 4(98) 2009, с. 22-30.
  • [13] Philips P.J., Wechler H., Huang J., Rauss P. The FERET Database and Evaluation Procedure for Face Recognition algorithms. Image and Vision Computing, Vol. 16, No. 5, 1998, pp. 295-306.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS3-0014-0056
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