Correlation based Fingerprint Image Segmentation

Abstract

In this paper, a correlation based fingerprint image segmentation technique is presented. Segmented is a useful image processing tool generally used in Machine vision applications in the Pattern Recognition and classification that leads to the separation of area of interest (AOI) from an image based on some image parameters like gray levels. In this proposed method, direction based segmentation is presented without computing and use of any directional field whose effectiveness has been evaluated for several finger print images (databases like FVC2002, FVC2000). KEYWORDSSegmentation, Fingerprint, Area of Interest (AOI), Correlation LITERATURE SURVEYThe foreground in a fingerprint image consists of oriented pattern with variations in orientations. In practice the presence of noise requires more robust segmentation techniques. Initially local or global thresholding techniques [3] used to segment foreground in a fingerprint image. Mehtre et al [4,5] isolated the fingerprint area based on the local histograms of ridge orientations. Ridge orientation is computed at each pixel and a histogram is computed for each 16 × 16 block. The presence of significant peaks in a histogram indicated an oriented pattern i.e. foreground, whereas a flat histogram characterize a background [4]. Mehtre et al proposed a composed method of segmentation in [5]. The local histograms of orientations and the gray-scale variance of each block have been used for segmentation. In the absence of reliable information from the histograms, assigns the lowvariance blocks to the background. This algorithm can handle the images where the background is uniform (white block in the background). Ratha et al [6] assigned each 16 × 16 block to the foreground or background according to the variance of gray-levels in an orthogonal direction to the ridge orientation. The background has no directional dependence and has uniform variance. The foreground has very high variance in a direction orthogonal to the ridge and very low variance along the ridges. Maio et al [7] separated the area of interest based on the average magnitude of the gradient in each image block. The fingerprint area is rich in edges due to ridges and valleys, the gradient response is high in the fingerprint area and small in the background. In the method proposed by Shen et al [8], eight Gabor filters are convolved with each image block, and the variance of the filter responses is used for fingerprint segmentation according to their quality, as "good," "poor," "smudged," or "dry." Bazen et al [9] proposed a pixel-wise segmentation technique. Where three features gradient coherence, intensity mean, and intensity variance are computed for each pixel and a linear classifier associates the pixel with the background or the foreground. A supervised technique is used to learn the optimal parameters for the linear classifier for each specific acquisition sensor. A final morphological post-processing step as suggested by Gonzalez et al [10] is performed to eliminate holes in both the foreground ...

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“…Characteristics such as gradient cohrence and ridge direction can be classified into gradient based characteristics. The gradient coherence and ridge direction features are calculated by (16) and (20) respectively.…”

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“…The gradient is used to obtain the directional variation of the intensity value along with an image I mg direction of size x Xy. Characteristics like gradient coherence and direction of the ridge can be classified into characteristics based on gradient.The coherence of the gradient and the direction of the ridge are calculated respectively by (16) and (20).…”