Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection.

ABSTRACT: We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of t

SUBMITTER: Gottschlich C 

PROVIDER: S-EPMC4742063 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature