Quality Estimation Algorithm Based on Learning for High-Resolution Palmprint Minutiae
Author:
Affiliation:

  • Article
  • | |
  • Metrics
  • |
  • Reference [16]
  • |
  • Related
  • |
  • Cited by
  • | |
  • Comments
    Abstract:

    While minutiae is important for high-resolution palmprint matching, the quality of minutiae is affected by principal lines, creases and other noises, and therefore it is necessary to estimate the quality of minutiae and to exclude poor minutiae. In this paper, a minutiae quality estimation algorithm based on learning for high-resolution palmprint is proposed. First, a series of features obtained by applying Gabor convolution, complex filtering, etc., are used to describe the local area of minutiae redundancy. Then, with each feature as a weak classifier, AdaBoost algorithm is applied in multi-layered training to identify the best features for discriminating minutiae. Finally, the response of weighted linear combination of weak classifiers is used as minutiae quality score, and minutiae with score above the threshold is selected as true minutiae. Comparing with the method based on Fourier transform response, the presented method is superior at distinguishing true from false minutiae, and provides better evaluation of minutiae quality.

    Reference
    [1] Lu G, Zhang D, Wang K. Palmprint recognition using eigenpalms features. Pattern Recognition Letters, 2003,24(9):1463~1467.
    [2] Kong A, Zhang D, Kamel M. A survey of palmprint recognition. Pattern Recognition, 2009,42(7):1408~1418. [doi: 10.1016/j.patcog.2009.01.018]
    [3] Chen F, Huang X, Zhou J. Hierarchical minutiae matching for fingerprint and palmprint identification. IEEE Trans. on Image Processing, 2013,22(12):4964~4971. [doi: 10.1109/TIP.2013.2280187]
    [4] Liu E, Jain AK, Tian J. A coarse to fine minutiae-based latent palmprint matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2013,35:2307~2322. [doi: 10.1109/TPAMI.2013.39]
    [5] Jain AK, Feng J. Latent palmprint matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009,31(6):1032~1047. [doi: 10.1109/TPAMI.2008.242]
    [6] Lim E, Jiang X, Yau W. Fingerprint quality and validity analysis. In: Mercer B, ed. Proc. of the 2002 IEEE 9th Int’l Conf. on Image Processing. Los Alamitos: IEEE Computer Society Press, 2002. I-469~I-472. [doi: 10.1109/ICIP.2002.1038062]
    [7] Shen L L, Kot A, Koo W M. Quality measures of fingerprint images. In: Bigun J, Smeraldi F, eds. Proc. of the Audio- and Video- Based Biometric Person Authentication. New York: Springer-Verlag, 2001. 266~271. [doi: 10.1007/3-540-45344-X_39]
    [8] Chen Y, Dass SC, Jain AK. Fingerprint quality indices for predicting authentication performance. In: Kanade T, Jain AK, Ratha NK, eds. Proc. of the Audio- and Video-Based Biometric Person Authentication. New York: Springer-Verlag, 2005. 160~170. [doi: 10.1007/11527923_17]
    [9] Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1997,55(1):119~139. [doi: 10.1006/jcss.1997.1504]
    [10] Gao X, Chen X, Cao J, Deng Z, Liu C, Feng J. A novel method of fingerprint minutiae extraction based on Gabor phase. In: Law B, ed. Proc. of the 2010 IEEE 17th Int’l Conf. on Image Processing. Los Alamitos: IEEE Computer Society Press, 2010. 3077~3080. [doi: 10.1109/ICIP.2010.5648893]
    [11] Liu C, Cao J, Gao X, Fu X, Feng J. A novel fingerprint matching algorithm using minutiae phase difference feature. In: Dooms A, Piater JH, eds. Proc. of the 2011 IEEE 18th Int’l Conf. on Image Processing. Los Alamitos: IEEE Computer Society Press, 2011. 3201~3204. [doi: 10.1109/ICIP.2011.6116349]
    [12] Johansson B. Low Level Operations and Learning in Computer Vision. Department of Electrical Engineering, Linköpings Universitet, 2004.
    [13] Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 1996,29(1):51~59. [doi: 10.1016/0031-3203(95)00067-4]
    [14] Viola P, Jones MJ. Robust real-time face detection. Int’l Journal of Computer Vision, 2004,57(2):137~154. [doi: 10.1023/B:VISI.0000013087.49260.fb]
    [15] Lim E, Toh KA, Suganthan PN, Jiang X, Yau WY. Fingerprint image quality analysis. In: Koh F, ed. Proc. of the 2004 IEEE 11th Int’l Conf. on Image Processing. Los Alamitos: IEEE Computer Society Press, 2004. 1241~1244.
    [16] Van Rijsbergen CJ. Information Retrieval. 2nd ed., London: Butterworths, 1979.
    Related
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

王瀚,刘重晋,付翔,封举富.基于学习的高分辨率掌纹细节点质量评价方法.软件学报,2014,25(9):2180-2186

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:April 08,2014
  • Revised:May 14,2014
  • Online: September 09,2014
You are the first2037990Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063