Abstract
Automated recognition of a person is one of the most critical issues in the modern society. Common biometric systems rely on the surface topography of an object and, thus, are potentially vulnerable for spoofing. Optical coherence tomography is a technology that has the capability to probe the internal structure of multilayered tissues. The paper describes an algorithm for automation fingerprint recognition that the algorithm is applied on the OCT fingerprint images. This algorithm is based on scanning of the enhanced and segmented OCT images.
Highlights
Automated or semi-automated recognition of a person is one of the critical issues
In order to decide whether an edge has been found, we have applied hysteresis thresholding for segmentation of image (Figure 5) for recognition of the fingerprint that weather it is faked by artificial layer or not, the segmented Optical Coherence Tomography (OCT) image is scanned for finding continuous line with white pixel if it is existed
OCT systems have been shown to be effective for reliable fingerprint identification by high resolution imaging of internal structures
Summary
Automated or semi-automated recognition of a person is one of the critical issues. Fingerprint recognition is one of the most commonly used methods of biometrics. Fingerprint recognition is cheap and relatively robust biometric method and it has been extensively studied and applied for many popular applications. These methods rely on the surface topography of the finger and are vulnerable for spoofing with using artificial fingerprint [2,3,4]. Presence of speckle noise results in granular appearance of the image, which in turn can obscure small or low reflectivity features, degrading the image quality of OCT tomograms It can impede or limit the performance of image segmentation and pattern recognition algorithms that are used to extract, analyze, and recognize diagnostically relevant features. The prepared segmented image is scanned for identification of additional layer
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