Abstract: Biometric identification systems based on fingerprints are vulnerable to attacks that use fake replicas of real fingerprints.

Fingerprint images are first segmented to discard background information. Then, small-sized foreground patches are extracted and processed by popular ...

This work presents a fingerprint liveness detection method that combines a patch-based voting approach with Transfer Learning techniques, and shows the ...

This study proposes a fingerprint liveness- detection method based on convolutional neural network (CNN) features extracted from fingerprint patches.

This paper presents a fingerprint liveness detection method founded on a patch--based voting approach. Fingerprint images are first segmented to discard ...

More specifically, convolutional neural networks (CNNs) and deep belief networks (DBNs) have been used for fingerprint PAD purposes, based either on the ...

The proposed convolutional neural network (CNN) structure applies the Fire module of SqueezeNet, and the fewer parameters used require only 2.0 MB of memory.

In this study, a CNN-based model was developed to perform liveness detection on given fingerprints. The performance of texture descriptors like LBP, LPQ ...

Fingerprint liveness detection using CNN features of random sample patches ... A DCNN based fingerprint liveness detection algorithm with voting strategy.

The final spoof detection is based on the majority voting system, which adds up all the block result and classifies input fingerprint image as fake or live.