Fingerprint Enhancement And Texture Matching Based On Gabor Filter

Automatic fingerprint identification technique has become a research focus in the fields of biometrics in last decades. And because of its credibility and stability, automatic fingerpnnt identification has been widely used in biometrics. Extract features exactly and credibility is the premise and basics of the automatic fingerprint identification system, moreover fingerpnnt enhancement and matching have direct influence on the veracity of feature extraction and the final matching result. This paper studies fingerprint enhancement and texture matching combining with the gabor filter.This paper mainly deals with fingerprint enhancement and texture matching. Fingerprint enhancement include: fingerprint enhancement combining fingerprint image quality and gabor filter, fingerprint enhancement combining finger code feature vector and gabor filter. Texture matching include: Implement and improvement the traditional filter based matching, texture matching using fingerprint image quality.Fingerprint enhancement combining with fingerprint image quality and gabor filter: Estimate the ridge distance of fingerprint based on spectrum analysis with big window and by use fingerprint image quality into enhancement, we proposed fingerpnnt enhancement combining with fingerprint image quality and gabor filter. First, estimate the ridge distance of fingerpnnt based on spectrum analysis with big window, construct eight gabor filters in eight different directions; then filter the image in eight directions and divide the eight filtered images into square sectors ; finally using fingerprint image quality to compose the finally enhanced image. Expenmental results demonstrate that, the method conquers the shortage of traditional enhancement in pattern areas, and is robust to fingerpnnt images of low quality.Fingerpnnt enhancement: Estimate the ndge distance of fingerpnnt based on spectrum analysis with big window and by use finger code feature vector into enhancement, we proposed fingerprint enhancement combining with finger code feature vector and gabor filter. First, estimate the ndge distance of fingerprint based on spectrum analysis with big window, construct eight gabor filters in eight different directions; then filter the image in eight directions and divide the eight filtered images into square sectors ; finally using finger code feature vector to compose the finally enhanced image. Expenmental results demonstrate that, the method not only possess the advantages of fingerpnnt enhancement combining fingerpnnt image quality and gabor filter, but also reduce the chance of bad blocks in fingerprint images.Texture matching: For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. The proposed filter-based texture matching uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length Finger Code. The fingerprint matching is based on the Euclidean distance between the two corresponding Finger Codes. First determine a reference point and region of interest for the fingerprint image; tessellate the region of interest around the reference point; then filter the region of interest in eight different directions using a bank of Gabor filters; finally compute the average absolute deviation from the mean of gray values in individual sectors in filtered images to define the feature vector or the Finger Code. Expenmental results demonstrate that the improved texture matching can identify the input fingerprints effectivelyTexture matching using fingerprint image quality: we try to introduce new feature vector into texture matching. The fingerprint matching is based on the Euclidean distance between the two corresponding Finger Codes. First determine a reference point and region of interest for the fingerprint image; tessellate the region of interest around the reference point; then filter the region of interest in eight different directions using a bank of Gabor filters; finally compute the fingerprint image qualityin individual sectors in filtered images to define the feature vector or the Finger Code.