Fingerprint recognition systems are one of the oldest biometric recognition systems, which are under constant development. Presence of noise in fingerprint severely affects performance of recognition systems. Fingerprint segmentation is one of the important part in recognition systems; they eliminate noisy background in the image improving performance of fingerprint recognition systems.

This thesis focuses on proposing a new algorithm for segmentation of fingerprint images using data clustering technique known as spectral clustering. The algorithm is based on block wise segmentation of fingerprint images. The criteria considered for segregating blocks into foreground and background are variance and double gradient of the image. The data in the blocks is clustered using spectral clustering technique.

Performance of the developed algorithm is evaluated using FVC2000, FVC2002, FVC2004 databases. Two sub-databases from each database are selected and relative sizes of the masks are compared to the hand annotated masks of the respective databases. From results, it is observed that double gradient is slightly better than variance in using as parameter for performing fingerprint segmentation