  Fingerprint recognition requires a minimal effort from the user, does not
capture other information than strictly necessary for the recognition process,
and provides relatively good performance. A critical step in fingerprint
identification system is thinning of the input fingerprint image. The
performance of a minutiae extraction algorithm relies heavily on the quality of
the thinning algorithm. So, a fast fingerprint thinning algorithm is proposed.
The algorithm works directly on the gray-scale image as binarization of
fingerprint causes many spurious minutiae and also removes many important
features. The performance of the thinning algorithm is evaluated and
experimental results show that the proposed thinning algorithm is both fast and
accurate. A new minutiae-based fingerprint matching technique is proposed. The
main idea is that each fingerprint is represented by a minutiae table of just
two columns in the database. The number of different minutiae types
(terminations and bifurcations) found in each track of a certain width around
the core point of the fingerprint is recorded in this table. Each row in the
table represents a certain track, in the first column, the number of
terminations in each track is recorded, in the second column, the number of
bifurcations in each track is recorded. The algorithm is rotation and
translation invariant, and needs less storage size. Experimental results show
that recognition accuracy is 98%, with Equal Error Rate (EER) of 2%. Finally,
the integrity of the data transmission via communication channels must be
secure all the way from the scanner to the application. After applying Gaussian
noise addition, and JPEG compression with high and moderate quality factors on
the watermarked fingerprint images, recognition accuracy decreases slightly to
reach 96%.
