Enhanced Secure Algorithm for Fingerprint Recognition

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%.