In this work, we investigate if previously proposed CNNs for fingerprint pore detection overestimate the number of required model parameters for this task. We show that this is indeed the case by proposing a fully convolutional neural network that has significantly fewer parameters. We evaluate this model using a rigorous and reproducible protocol, which was, prior to our work, not available to the community. Using our protocol, we show that the proposed model, when combined with post-processing, performs better than previous methods, albeit being much more efficient. All our code is available at this https URL
Wang al @cite_1 use a U-Net to detect pores in fingerprint images. The U-Net is trained to classify each spatial location in the image into one of three categories: pore centroid, ridge or background. To detect pores, 20 patches are extracted from the input image and each is forwarded through the trained CNN. This results in 3 probability maps per patch, one indicating the probability of pores, the other of ridges and the last of background region. The ridge probability map for each patch is used to post-process the one for pores. Afterward, the pore probability maps are binarized. To combine the predictions for each patch, a boolean or'' operation is performed with all of them.
@cite_1
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The public demand for personal safety is increasing rapidly. Fingerprint features as the most commonly used bio-signature need to improve their safety continuously. The third level features of fingerprint (especially the sweat pores) can be added to the automatic fingerprint recognition system to increase the accuracy of fingerprint identification in a variety of environments. Due to perspiration activities, the shape and size sweat of pores are varying spatially and temporally. Extraction of fingerprint pores is both critical and challenging. In this paper, we adapt a novel fully convolutional neural network called U-net for ridges and sweat pores extraction. The PolyU High-Resolution-Fingerprint (HRF) database is used for testing of the proposed method. The results show the validity of the proposed method. With the majority of the pores correctly extracted, the proposed method can serve for fingerprint recognition using Level 3 features.