Residual Squeeze-and-Excitation U-Shaped Network for Minutia Extraction in Contactless Fingerprint Images

The COVID-19 pandemic has impacted research directions, especially on biometrics. Unfortunately, contact fingerprinting is still widely used and can be a pitfall for spreading the virus disease to the public. On the good side, this pitfall renews the interest in user-acceptance contactless fingerprinting as an alternative to the old and widely used contact sensors. Due to the low contrast produced by touchless images, developing a reliable minutia extraction method remains a challenge. This work proposes and analyzes a residual squeeze-and-excitation U-shaped deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three public datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial tools. Our results show that the proposed method can achieve an improvement of up to 2.1 percentage points in the F1-score over the existing ones, which can lead to a decrease in the equal error rate of up to 1.42 percentage points in verification experiments.