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Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation


It is important to estimate the ridge distance accurately, an intrinsic texture property of a fingerprint image. Up to now, only several articles have touched directly upon ridge distance estimation. Little has been published providing detailed evaluation of methods for ridge distance estimation, in particular, the traditional spectral analysis method applied in the frequency field. In this paper, a novel method on nonoverlap blocks, called the statistical method, is presented to estimate the ridge distance. Direct estimation ratio (DER) and estimation accuracy (EA) are defined and used as parameters along with time consumption (TC) to evaluate performance of these two methods for ridge distance estimation. Based on comparison of performances of these two methods, a third hybrid method is developed to combine the merits of both methods. Experimental results indicate that DER is 44.7%, 63.8%, and 80.6%; EA is 84%, 93%, and 91%; and TC is,, and seconds, with the spectral analysis method, statistical method, and hybrid method, respectively.

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Correspondence to Yilong Yin.

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Yin, Y., Tian, J. & Yang, X. Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation. EURASIP J. Adv. Signal Process. 2004, 694950 (2004).

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  • fingerprint
  • ridge distance
  • spectral analysis
  • statistical window
  • hybrid method