Towards Fingerprint Spoofing Detection in the Terahertz Range

Abstract

Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger pad is described with a focus on the outermost stratum corneum. We identified and characterized five representative spoofing materials and prepared thin and thick finger imitations. The complex refractive index of the materials was determined in TDS in the transmission configuration. For dataset collection, we selected a group of 16 adults of various ages and genders. The reflection results were analyzed both in the time (reflected signal) and frequency (reflectivity) domains. The measured signals were positively verified with the theoretical calculations. The signals corresponding to samples differ from the finger-related signals, which facilitates spoofing detection. Thanks to deconvolution, we provide a basic explanation of the observed phenomena. We propose two spoofing detection methods, predefined time–frequency features and deep learning based. The methods achieved high true detection rates of 87.9% and 98.8%. Our results show that the terahertz technology can be successfully applied for spoofing detection with high detection probability.

A comprehensive survey of fingerprint presentation attack detection

“…To facilitate the exploration of novel fingerprint PAD techniques involving both hardware and software, Engelsma et al [42] designed and prototyped a low-cost custom fingerprint reader, known as RaspiReader, with ubiquitous components. RaspiReader has two cameras for fingerprint image acquisition.…”

The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme

“…Goicoechea-Telleria et al [16] proposed using microscope imaging with special lighting conditions to acquire fingerprint presentations then investigated the scale invariant feature transform (SIFT) through the bag of words approach in order to extract the PAD features; the experiment reported an APCER of 1.78 at 1.33% BPCER. Norbert and Kowalski [17] used time domain spectroscopy setup in the reflection configuration to study the interaction of terahertz radiation with the friction ridge skin of the finger. A deep learning model was implemented to classify attacks and bona fide presentation achieving 98.8% classification accuracy.…”

Vascular Auscultation of Carotid Artery: Towards Biometric Identification and Verification of Individuals

“…However, there are still drawbacks and limitations of these methods and systems. As a unique trait, fingerprints suffer from drawbacks such as aging [ 6 ], skin disease [ 7 ], amputations [ 8 ], and spoofing with latex and modeling clay [ 9 ]. While an authentic voice recording could be used for unauthorized access, a voice sensing system is not efficient in cases where there is damage in vocal cords, infection in the larynx, or for a person with voice impairment, thus making the identification difficult.…”