A Novel Fingerprint Segmentation Method by Introducing Efficient Features and Robust Clustering Assignment Technique

Fingerprint recognition systems hold a pivotal role across various modern applications, such as criminal investigation, civil identification, and access control. Fingerprint segmentation is commonly the first stage of fingerprint recognition systems, focusing on extracting the foreground from captured fingerprint images. This helps to reduce extracting false minutiae, speed up the extraction process, and hence, improve the overall system performance. In this paper, we introduce a novel fingerprint segmentation method by first proposing new frequency and intensity features with employing fuzzy-c-means and genetic algorithm, accompanied by proposing a new cluster assignment strategy that involves features weighting and cluster assignment probabilities thresholding which helps to improve the accuracy compared to the standard cluster assignment method. Finally, we introduce a new morphological post-processing technique in order to minimize the misclassified pixels and obtain the final mask. Furthermore, we carry out a comprehensive performance evaluation against leading fingerprint segmentation methods on various known databases. As a result, the proposed method achieves an average error rate of 2.52%, which is much lower than the corresponding errors obtained in the literature. By these experimental results, we show that the proposed method outperforms the best existing methods.