Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Preselection Using Eigenfeatures
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Automatic Fingerprint Recognition Systems
Automatic Fingerprint Recognition Systems
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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 eigen-features amenable to DP matching. The first PCA extracts eigenfeatures of Fourier spectra from each image line. The second extracts eigen features 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.