Classification of Live Scanned Fingerprints using Dense SIFT based Ridge Orientation Features

Fingerprint recognition is a time consuming process in huge fingerprint data-bases. Classifying fingerprints and narrowing the search to a specific class is an effective way to reduce the processing time. For the classification of noisy fingerprints acquired by live scan devices, we proposed a new scheme, based on statistical features derived from dense scale invariant feature transform (d-SIFT). Dense SIFT descriptor is not computed for certain key points but for all pixels of a fingerprint. Each SIFT descriptor consists of 128 features and the distribution of each feature over all SIFT descriptors extracted from a finger-print is used to compute statistical features (mean, standard deviation, kurtosis and skewness). In this approach the dimension of the feature space remains fixed (4×128) irrespective of the number of SIFT descriptors. Extreme Learning Machine (ELM) with radial basis function (RBF) kernel is used as a classifier. The proposed scheme has been evaluated using FVC2004, a bench-mark database of noisy low quality live scanned fingerprints; it achieved the average accuracy of 97.63, which is better than the state-of-the-art fingerprint classification methods.