Segmentation of Fingerprint Images Using Support Vector Machines

Fingerprint segmentation is an important step in fingerprint identification processing. Exact segmentation of fingerprint image is very important for fingerprint singular points and minutiae features extraction. In this paper, we present a fingerprint image segmentation algorithm based on support vector machines (SVM). At first, the image is partitioned into 12 times 12 blocks and the low gray variance background blocks were segmented by the contrast. After that, the coherence and the main energy ratio were extracted on the left blocks. We use supervised SVM to classify patterns and select typical patterns to train the classifier. The blocks that can not be decided by the first segmentation were segmented by a SVM classifier. Finally, morphology was applied as postprocessing to reduce the number of classification errors. Experimental results demonstrate that the proposed method is effective and robust.