Fingerprint Segmentation Based on Adaptive and Orientation Algorithm

Abstract

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. KEYWORDS:Image recognition, Fingerprint Segmentation, Fingerprint Image Pre-processing, Total Variation, Ridge Orientation. I. INTRODUCTIONA biometric is any physical or behavioral trait that can be used to identify a person. The most common biometric traits are face, speech, fingerprint, and iris. Fingerprints are one of the most widely used traits due to their universality, distinctiveness and performance. From the creation of the first Automated Fingerprint Identification System (AFIS), a tremendous amount of effort and progress has been made to match sample prints accurately and consistently with large fingerprint databases. An essential step towards achieving accurate identification is an accurate segmentation.On the basis of collection procedure, fingerprint images can generally be classified into three categories, namely, rolled, plain and latent [3]. Rolled fingerprints are obtained from rolling the finger from one side to the other in order to capture all ridge details of the fingerprint. Plain fingerprints images are acquired by pressing the fingertip onto a flat surface. Since rolled and plain prints are obtained in an attended mode, so they are usually of good visual quality and contain sufficient information for reliable matching. However, latent fingerprints are usually collected from crime scenes, in which the print is lifted from object surfaces that were inadvertently touched or handled. The matching between latent and rolled/plain fingerprints plays a crucial role in identifying suspects by law enforcement agencies.Fingerprint segmentation is part of pre-processing in fingerprint image recognition system and it refers to the process of decomposing a fingerprint image into two disjoint regions: foreground and background. The foreground, also called the region of interest (ROI), consists of the desired fingerprints while the background contains noisy and irrelevant contents that will be discarded in the following proce...