Singular points detection with semantic segmentation networks
This work addresses the challenge of improving detection rates for singular points in fingerprint recognition, which is critical for biometric applications, but it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of singular points detection in fingerprint recognition, particularly for low-quality fingerprints, by treating it as a semantic segmentation task using a new CNN called SinNet and achieves an 11% increase in correctly detected fingerprints and over 18% in core detection rate compared to state-of-the-art methods.
Singular points detection is one of the most classical and important problem in the field of fingerprint recognition. However, current detection rates of singular points are still unsatisfactory, especially for low-quality fingerprints. Compared with traditional image processing-based detection methods, methods based on deep learning only need the original fingerprint image but not the fingerprint orientation field. In this paper, different from other detection methods based on deep learning, we treat singular points detection as a semantic segmentation problem and just use few data for training. Furthermore, we propose a new convolutional neural network called SinNet to extract the singular regions of interest and then use a blob detection method called SimpleBlobDetector to locate the singular points. The experiments are carried out on the test dataset from SPD2010, and the proposed method has much better performance than the other advanced methods in most aspects. Compared with the state-of-art algorithms in SPD2010, our method achieves an increase of 11% in the percentage of correctly detected fingerprints and an increase of more than 18% in the core detection rate.