Robert K. Rowe

Spoof Detection Schemes

Biometrics is defined as an automated method of verifying or recognizing the identity of a living person based on physiological or behavioral characteristics [1]. While much research has been done both to determine which traits can differentiate humans and to optimize that differentiation, the problem of determining if the presented trait originates from a living person has received relatively less attention. Between acquiring biometric data and delivering a result, there are various points where the overall security of a biometric access system can be compromised.

Multispectral fingerprint biometrics

A novel fingerprint sensor is described that combines a multispectral imager (MSI) with a conventional optical fingerprint sensor. The goal of this combination is a fingerprint sensor with improved usability and security relative to standard technology. The conventional sensor that was used in this research is a commercially available system based on total internal reflectance (TIR). It was modified to accommodate an MSI sensor in such a way that both MSI and TIR images are able to be collected when a user places his/her finger on the sensor platen. The MSI data were preprocessed to enhance fingerprint features. Both the preprocessed MSI images and the TIR images were then passed to a commercial fingerprint software package for minutiae detection and matching. A multiperson study was conducted to test the relative performance characteristics of the two types of finger data under typical office conditions. Results demonstrated that the TIR sensor performance was degraded by a large number of poor quality fingerprint images, likely due to a large percentage of samples taken on people with notably dry skin. The corresponding MSI data showed no such degradation and produced significantly better results. A selective combination of both modalities is shown to offer the potential of further performance improvements.

Multispectral Fingerprint Image Acquisition

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 Finger Skin Histology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 MSI Principles of Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 Composite Fingerprint Image Generation . . . . . . . . . . . . . . . . . . . . . . . . 8 Biometric Testing and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10 Baseline Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10 Comparative Performance under Adverse Influences . . . . . . . . . . 11 Backward Compatibility with Legacy Data . . . . . . . . . . . . . . . . . . . 13 Spoof Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14 Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Novel spectroscopy-based technology for biometric and liveness verification

This paper describes a new biometric technology based on the optical properties of skin. The new technology can perform both identity verification and sample authenticity based on the optical properties of human skin. When multiple wavelengths of light are used to illuminate skin, the resulting spectrum of the diffusely reflected light represents a complex interaction between the structural and chemical properties of the skin tissue. Research has shown that these spectral characteristics are distinct traits of human skin as compared to other materials. Furthermore, there are also distinct spectral differences from person to person. Personnel at Lumidigm have developed a small and rugged spectral sensor using solid-state optical components operating in the visible and very near infrared spectral region (400-940nm) that accurately measures diffusely reflected skin spectra. The sensors are used for both biometric determinations of identity as well as the determination of sample authenticity. This paper will discuss both applications of the technology with emphasis on the use of optical spectra to assure sample authenticity.

Multispectral fingerprint imaging for spoof detection

Fingerprint systems are the most widespread form of biometric authentication. Used in locations such as airports and in PDAs and laptops, fingerprint readers are becoming more common in everyday use. As they become more familiar, the security weaknesses of fingerprint sensors are becoming better known. Numerous websites now exist describing in detail how to create a fake fingerprint usable for spoofing a biometric system from both a cooperative user and from latent prints. While many commercial fingerprint readers claim to have some degree of spoof detection incorporated, they are still generally susceptible to spoof attempts using various artificial fingerprint samples made from gelatin or silicone or other materials and methods commonly available on the web. This paper describes a multispectral sensor that has been developed to collect data for spoof detection. The sensor has been designed to work in conjunction with a conventional optical fingerprint reader such that all images are collected during a single placement of the finger on the sensor. The multispectral imaging device captures sub-surface information about the finger that makes it very difficult to spoof. Four attributes of the finger that are collected with the multispectral imager will be described and demonstrated in this paper: spectral qualities of live skin, chromatic texture of skin, sub-surface image of live skin, and blanching on contact. Each of these attributes is well suited to discriminating against particular kinds of spoofing samples. A series of experiments was conducted to demonstrate the capabilities of the individual attributes as well as the collective spoof detection performance.

Multispectral imaging for biometrics

Automated identification systems based on fingerprint images are subject to two significant types of error: an incorrect decision about the identity of a person due to a poor quality fingerprint image and incorrectly accepting a fingerprint image generated from an artificial sample or altered finger. This paper discusses the use of multispectral sensing as a means to collect additional information about a finger that significantly augments the information collected using a conventional fingerprint imager based on total internal reflectance. In the context of this paper, “multispectral sensing” is used broadly to denote a collection of images taken under different polarization conditions and illumination configurations, as well as using multiple wavelengths. Background information is provided on conventional fingerprint imaging. A multispectral imager for fingerprint imaging is then described and a means to combine the two imaging systems into a single unit is discussed. Results from an early-stage prototype of such a system are shown.

Robust fingerprint acquisition: a comparative performance study

A comparative study on multiple participants was undertaken to quantify the ability of a multispectral imaging fingerprint sensor to perform reliable biometric matching in the presence of extreme sampling conditions. These extreme conditions included finger wetness, dirt, chalk, acetone, bright ambient light, and low contact pressure during image acquisition. The comparative study included three commercially available total internal reflectance sensors, run in parallel with the multispectral imaging sensor and under identical sampling conditions. Performance assessments showed that the multispectral imaging sensor was able to provide fingerprint images that produced good biometric performance even under conditions in which the performance of the total internal reflectance sensors was severely degraded. Additional analysis showed that the performance advantage of the multispectral images taken under these conditions was maintained even when matched against enrollment images collected on total internal reflectance sensors.