# FDSNet: Finger dorsal image spoof detection network using light field camera

## TL;DR

This paper introduces FDSNet, a deep learning-based system using light field camera images to detect spoofing attacks on finger dorsal biometric authentication, demonstrating high effectiveness against various attack types.

## Contribution

It presents the first feasibility study of spoofing detection on finger dorsal images using a CNN with transfer learning, and introduces a new dataset of real and spoof images.

## Key findings

- Proposed method outperforms existing techniques in spoof detection accuracy.
- Effective detection of multiple spoofing attack types including printed and scanned images.
- Demonstrated feasibility of using light field camera images for biometric security.

## Abstract

At present spoofing attacks via which biometric system is potentially vulnerable against a fake biometric characteristic, introduces a great challenge to recognition performance. Despite the availability of a broad range of presentation attack detection (PAD) or liveness detection algorithms, fingerprint sensors are vulnerable to spoofing via fake fingers. In such situations, finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications. In this paper, we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system, which include four types of presentation attacks such as printed paper, wrapped printed paper, scan and mobile. This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism. We have collected 196 finger dorsal real images from 33 subjects, captured with a Lytro camera and also created a set of 784 finger dorsal spoofing images. Extensive experimental results have been performed that demonstrates the superiority of the proposed approach for various spoofing attacks.