Fingerprints are widely used for biometric validation worldwide. Since human fingerprints are unique and remain constant over time, it provides an easy-to-use, reliable, and economical authentication method. In addition to that, fingerprint recognition systems are of great importance because of their applicability in our lives. This work presents a classification methodology based on Henry Classification System using Convolutional Neural Networks (CNNs) models, such as Darknet, Alexnet, Resnet, VGG16, and Deep Belief Network. Besides that, we evaluate our proposal by carrying out experiments using grayscale images and pre-processed images as input on the classification step with the combination of the Gabor filter and the morphological thinning operation. We have obtained the highest result accuracy of 95.1% in the NIST Special Database 4, a widespread fingerprint dataset, using the Resnet 34 model in grayscale images. The proposed approach was evaluated with extraction strategies of classic attributes and based on convolutional networks. According to the results, the proposed methodology presents promising results, surpassing the traditional approaches present in the literature.

Fingerprint Classification based on the Henry System via ResNet



**Session chairs**:   Sonja Grgić and Juraj Kačur




Session: Machine Learning

technical paper


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João Wellington Mendes de Souza

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Fingerprint Classification based on the Henry System via ResNet

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