Abstract
The increasing prominence of fingerprint recognition as a biomet ric identifier has made it more vulnerable to presentation attacks, specifically non-distal attacks that exploit ridge and minutiae pat terns foundinnon-distalphalanges.Inthisstudy,wepresentpresen tation attacks through non-distal/toe prints and a state-of-the-art lightweight inverted residual network that excels at differentiating between distal and non-distal prints, providing unrivaled perfor mance in terms of accuracy, inference time, and false negative rate (FNR). Our proposed model surpasses other statistical machine learning methods, such as variable-margin SVM, and lightweight models like MobileNet v2, MobileNet v3, and ResNet18. We meticu lously evaluate our model using a diverse array of datasets, includ ing the NIST dataset, an in-house collected dataset, a toe dataset, a synthetic dataset generated by VeriFinger software, and a six-class dataset. To assess performance when only minutiae points are avail able, we develop analgorithmthat converts fingerprints to minutiae points and subsequently reconstructs fingerprints. Furthermore, we examine the ridge density of distal and non-distal prints across datasets, emphasizing their similarities and underscoring the need for advanced detection techniques. To the best of our knowledge, this study represents the first endeavor to propose a solution for presentation attack detection in non-distal phalanges. Our research demonstrate various challenges of presentation attacks, the effectiveness of our approach, which holds the potential to significantly influence the domain of finger print recognition and security. By sharing our dataset, model, and experimental details with the research community, we aim to foster further advancements in this crucial area. Upon publication, we will make our dataset and experimental details available alongside the paper
