Abstrakt

Fingerprint Segmentation Based on Adaptive and Orientation Algorithm

Rohan Nimkar , Agya Mishra

Fingerprint image segmentation is part of pre-processing in fingerprint image recognition system. It has a critical effect to the fingerprint image recognition system. Regardless of years of research in the area of general purpose image segmentation, it is still a very challenging task. A new image decomposition scheme, called the Adaptive and Orientation algorithm, is proposed to achieve effective segmentation for fingerprint images in this work. The proposed model is inspired by total variation models, but it differentiates itself by integrating two unique features of fingerprints; namely, scale and orientation. The proposed Adaptive and Orientation algorithm decomposes a fingerprint image into two layers: cartoon and texture. The cartoon layer contains unwanted components (e.g. structured noise) while the texture layer mainly consists of the latent fingerprint. Proposed segmentation algorithm is experimented and analyzed for two different fingerprint images. The PSNR for image segmentation has been used as a comparison parameter for proposed image segmentation methods.