Spectral Eigenfeatures for Effective DP Matching in Fingerprint Recognition

摘要

Dynamic Programming (DP) matching has been applied to solve distortion in spectral-based fingerprint recognition. However, spectral data is redundant, and its size is huge. PCA could be used to reduce the data size, but leads to loss of topographical information in projected vectors. This allows only inter-vector similarity estimations such as Euclid or Mahalanobis distances, and proves to be inadequate in presence of distortion occurring in finger sweeping with a line sensor. In this paper, we propose a novel two-step PCA to extract compact eigenfeatures amenable to DP matching. The first PCA extracts eigenfeatures of Fourier spectra from each image line. The second extracts eigenfeatures from all lines to form the feature templates. In matching, the feature templates are inversely transformed to line-by-line representations on the first PCA subspace for DP matching. Fingerprint matching experiments demonstrate the effectiveness of our proposed approach in template size reduction and accuracy improvement.