However, our work demonstrates that there is a cost-effective way to protect against spoofing. In sum, current biometric fingerprint sensors can be fooled easily by spoof attacks. The added layer of security may provide a level of confidence that end-users require for greater market acceptance. Each method of detection can achieve acceptable results, but more important, different options can be tailored for the application and hardware. Fake fingers do not benefit from this technique because inherent live properties are absent. This strategy can prevent perspiration-saturated and smudged (noise added) fingerprints. We also found that a live subject can protect against a false rejection by wiping his finger on clothing and then placing it on the scanner with normal pressure. Our methods show variability in performance across technologies, possibly due to differences in the quality of fingerprint images from different scanners. We have evaluated our methods on capacitive DC, electro-optical, and several optical scanners. Integration of both detection methods can achieve almost perfect classification in our current data set (644 live and 570 spoof images). 5,6 The valley noise detection method 7 achieves an EER of approximately 5%. The ridge perspiration detection method achieves an equal error rate (ERR) of approximately 10% when distinguishing live from spoof images in a data set of 58 live, 80 spoof, and 25 cadaver images. Image and signal processing, and pattern-recognition algorithms can quantify both moisture and noise using wavelet and statistical approaches. However, spoof fingers have greater noise along the valleys because the materials are transmuted easily by human pressure and because granules collect along the valleys due to the properties of the materials. Live fingers demonstrate both dynamic and static moisture patterns along the ridges due to perspiration and pores. Extracted (a) ridge and (b) valley structures from a live fingerprint image. The final step classifies the image.įigure 2. Ridge and valley signals, wavelet coefficients, and an intensity histogram contribute to the creation of a feature set. Then the region of interest is extracted, which involves the core of the fingerprint, and ridge and valley segmentation. The first step checks image quality and cleans signal strength. Both of these approaches to liveness detection use the same algorithmic structure. Spoof fingers have a distinctive noise distribution-unlike the clean valley structure of live fingers-due to the spoofing material's properties when placed on a scanner. Live fingers, unlike spoof and cadaver fingers, have a distinctive spatial perspiration pattern when in physical contact with the capturing surface of a fingerprint scanner.
![hardware fingerprint fake hardware fingerprint fake](https://www.mdpi.com/mathematics/mathematics-08-00517/article_deploy/html/images/mathematics-08-00517-g001.png)
5–7 Figure 2 illustrates ridge and valley extraction from a live image.
![hardware fingerprint fake hardware fingerprint fake](https://d3i71xaburhd42.cloudfront.net/bec61422ae8c0b186272828be762fa36c44c82cb/2-Figure4-1.png)
#HARDWARE FINGERPRINT FAKE SKIN#
Images of typical (a) live and (b) spoof fingerprints.Īlthough other software-based liveness approaches evaluate skin deformation, 3,4 our group focuses on the perspiration pattern along fingerprint ridges and the noise pattern along valleys. Our software-based approach analyzes the patterns in a fingerprint image relating to measurable characteristics that differ between spoof materials and live fingers.įigure 1. Previous efforts used medical-based hardware measurements, such as from pulse oximetry, temperature, and odor.
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![hardware fingerprint fake hardware fingerprint fake](https://www.goodix.com/en/upload/ueditor/image/20161028/6361326179504420959927211.jpg)
To combat spoofing, methods of liveness detection measure physiological signs to ensure that only live fingers are captured for enrollment or authentication. 1,2 Figure 1 compares live and spoof fingerprint images. Recent research demonstrates that it is possible to spoof or deceive a variety of fingerprint scanners using simple molds made from plastic, clay, Play-Doh, silicone, or gelatin materials. Spoofing is an attack at the sensor level in which a biometric sample is replaced by an imposter's sample. Each of these scenarios leaves us open to attack. However, security problems arise when we display our passwords or keys, or when we deposit our fingerprints on the side of a drinking glass. They allow us to forget our passwords or where we left our keys because the information required to login or unlock something is encoded in our anatomy and behavior. Biometric systems are an exciting emerging technology.