Identification of Humans Using Robust Biometric Features

  • Byungjun Son
  • Jung-Ho Ahn
  • Ji-hyun Park
  • Yillbyung Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


The size of the feature set is normally large in a recognition system using biometric data, such as Iris, face, fingerprints etc. As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient biometric identification. In this paper, we present one of the major discriminative learning methods, namely, Direct Linear Discriminant Analysis (DLDA). Also, we specifically apply the multiresolution decomposition of 2-D discrete wavelet transform to extract the robust feature set of low dimensionality from the acquired biometric data and to decrease the complexity of computation when using DLDA. This method of features extraction is well suited to describe the shape of the biometric data while allowing the algorithm to be translation and rotation invariant. The Support Vector Machines (SVM) approach for comparing the similarity between the similar and different biometric data can be assessed to have the feature’s discriminative power. In the experiments, we have showed that that the proposed method for human iris and face gave a efficient way of representing iris and face patterns.


Support Vector Machine Feature Vector Linear Discriminant Analysis Discrete Wavelet Transform Biometric Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Watanabe, H., et al.: Discriminative Metric Design for Robust Pattern Recognition. IEEE Trans. On Signal Processing 45, 2655–2662 (1997)CrossRefGoogle Scholar
  2. 2.
    Jollife, I.T.: Principal Component Analysis. Springer, New York (1986)Google Scholar
  3. 3.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis. Wiley Interscience, Hoboken (2000)Google Scholar
  4. 4.
    Poston, W.L., Marchette, D.J.: Recursive Dimensionality Reduction Using Fisher’s Linear Discriminant. Pattern Recognition 31(7), 881–888 (1988)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Yu, H.: A Direct LDA Algorithm for High-Dimensional Data - with Application to Face Recognition. Pattern Recognition 34(10), 2067–2070 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Daugman, J.G.: High confidence visual recognition of persons by a test of statistical indenpendence. IEEE Trans. On Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  7. 7.
    Wiles, R.P.: Iris Recognition: An Emerging Biometric Technology. Proc. of the IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  8. 8.
    Boles, W.W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEETrans. of Signal Processing 46(4), 1185–1188 (1998)CrossRefGoogle Scholar
  9. 9.
    Ioammou, D., Huda, W., Laine, A.F.: Circle recognition through a 2D Hough Transform and radius histogramming. Image and Vision Computing 7, 15–26 (1999)CrossRefGoogle Scholar
  10. 10.
    Tefas, A., Kotropoulos, C., Pitas, I.: Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(7) (2001)Google Scholar
  11. 11.
    AT&T Laboratories Cambridge. The ORL Database of Faces,

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Byungjun Son
    • 1
  • Jung-Ho Ahn
    • 1
  • Ji-hyun Park
    • 1
  • Yillbyung Lee
    • 1
  1. 1.Division of Computer and Information EngineeringYonsei UniversitySeoulKorea

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