2018/11/14 by Gabriel Dahia, Dahia, Gabriel, Maurício Pamplona Segundo +1
Computer Science · Social Sciences · #Biometric Identification and Security #Adversarial Robustness in Machine Learning #Forensic Fingerprint Detection Methods
paper · pdf · doi:10.48550/arxiv.1811.06846
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 https://github.com/gdahia/fingerprint-pore-detection