Fingerprint Silicon Replicas: Static and Dynamic Features for Vitality Detection Using an Optical Capture Device

Abstract

The automatic vitality detection of a fingerprint has become an important issue in personal verification systems based on this biometric. It has been shown that fake fingerprints made using materials like gelatine or silicon can deceive commonly used sensors. Recently, the extraction of vitality features from fingerprint images has been proposed to address this problem. Among others, static and dynamic features have been separately studied so far, thus their respective merits are not yet clear; especially because reported results were often obtained with different sensors and using small data sets which could have obscured relative merits, due to the potential small sample-size issues. In this paper, we compare some static and dynamic features by experiments on a larger data set and using the same optical sensor for the extraction of both feature sets. We dealt with fingerprint stamps made using liquid silicon rubber. Reported results show the relative merits of static and dynamic features and the performance improvement achievable by using such features together.

Fingerprint liveness detection based on quality measures

“…proposed performs best on the dataset captured with the Biometrika sensor where, for the optimal feature subset, an ACE of 1.73% is reached (over 98% of correctly classified samples). This result clearly improves the one presented in [16] where, on a very similar dataset and using a parameterization based on different static and dynamic features (which need several images to be extracted), a best 17% classification error is reported (almost 10 times higher than the error rate reached with our proposed quality-based approach).…”

“…A comparative analysis of different software-based solutions for liveness detection is presented in [16]. The authors study the efficiency of several approaches and give an estimation of the best performing static and dynamic features for vitality detection.…”

A high performance fingerprint liveness detection method based on quality related features

“…Different liveness detection algorithms have been proposed for traits such as fingerprint [20][21][22], face [23][24][25], or iris [26][27][28]. These algorithms can broadly be divided into:…”

Spoofing Detection for Fingerprint using Hog and Iris using Surf Descriptor

“…And iris based systems can be faked with wearable plastic lens or by using fake irises printed on paper. [5] and they can be broadly divided into two approaches; Hardware and software. In the hardware approach a specific device is included to the sensor in order to detect the properties of a living trait such as the blood pressure [6], skin distortion [7] or the odor [8].The main disadvantage of these methods is that they require extra hardware, which is expensive, bulky and not convenient to the users.…”