1 research outputs found
FDSNet: Finger dorsal image spoof detection network using light field camera
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