Fingerprint Segmentation using the Phase of Multiscale Gabor Wavelets

Most automatic systems for fingerprint identification are based on minutiae matching. Minutiae points are terminaisons and bifurcations of the ridge lines that constitute a fingerprint pattern. A critical step in fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint image. The efficiency of minutiae detection depends on how well the ridges and valleys are extracted. The result of this segmentation process is a binarized image. In our present work, we propose a multiscale Gabor wavelet filter bank for a robust and efficient fingerprint segmentation. After a brief presentation of the Gabor wavelet theory, we explain how ridges and valleys are distinguished in terms of the phase, this being the key point of our binarization process. Moreover, the multiscale approach provides noise elimination whilst preserving singularities that characterize minutiae. Finally, we have evaluated the performance of our minutiae extraction algorithm using the accuracy of an online fingerprint verification system.