Fingerprint Spoof Detection Using Near Infrared Optical Analysis

Fingerprints have been used for several centuries as a means of identifying individuals. Since every fingerprint is considered to be unique, fingerprint recognition is the most popular biometric identification method currently employed in such areas as law enforcement, financial transactions, access control, and information security. Fingerprints consist of ridges and furrows on the surface of a fingertip. The ridges are the raised portions of the fingerprint while the furrows are the spaces between the ridges. Recognition can be performed based on ridge ending and ridge bifurcation (Xiao & Raffat, 1990), tessellated invariant moment (Yang & Park, 2008), and image-based features (Nanni & Lumini, 2009). Since the ridges are created by nature, people may consider that stealing and duplicating a fingerprint is more difficult than stealing a password or token, but it turns out that it is not difficult to make an artifact to fool an automated fingerprint system. It has been reported that an automated fingerprint authentication system could be defeated either using “a combination of low cunning, cheap kitchen supplies and a digital camera” , or simply by creating false thumbprint images . These sensor-level attacks are called “spoofing” attacks in which an artifact containing a copy of the fingerprint traits of a legitimate enrolled user is used to fool a fingerprint system. The first step is to obtain the fingerprint of a legitimate user, which can be accomplished by lifting a latent print either with or without the cooperation of the fingerprint owner. Next, molding plastic and gelatin can be used to make “gummy fingers”. Finally, the resulting fake fingers can be used to fool the fingerprint sensor and attack the security system. The vulnerability to fake-finger attack has generated a wave of research concerned with adding “liveness detection” to improve system resistance to spoofing. Liveness detection is the ability to determine whether a biometric sample is being provided by a live human being rather than from a copy created using an artifact. The detection methods can be categorized into two groups: hardware-based and software-based. In hardware-based solutions, extra hardware must be integrated with biometric sensors to detect additional information such as heartbeat, temperature, and the tissue under the epidermis. For example, an extra sensor can be used to measure either blood flow or pulse