Correlation pattern recognition for biometrics

Think of withdrawing cash from an automatic teller machine (ATM). What if a thief steals your ATM card and comes to know your personal identification number (PIN)? For many secure buildings, access is restricted to bearers of an appropriate magnetic swipe card or a radio frequency identification (RFID) tag. But what if that RFID tag or swipe card falls into the wrong hands? Similarly, when we login to computers, we rely on passwords that can be forgotten or stolen. In our increasingly security-conscious world, it is important to improve our ability to control access to physical (e.g., buildings) and virtual spaces based on who the person is, rather than on what she knows (e.g., passwords) or what he possesses (e.g., RFID tags). Biometric recognition offers this ability. Biometrics refers to the physiological (e.g., face, fingerprint, iris image, retinal patterns, DNA, etc.) or behavioral (e.g., gait, dynamic signature, keyboard dynamics, etc.) characteristics that distinguish one person from another. Thus, a given person has a unique biometric pattern since biometric signatures cannot be lost, forgotten, or easily stolen. It is of course possible to compromise biometric verification systems (e.g., using fake fingerprints made out of gelatinous materials) and to counter these efforts, liveness detection methods are being actively developed. Biometrics can be used to authenticate a person’s claimed identity (called verification or the 1:1 matching problem) or to ascertain whether that person is in a database or not (called identification or the 1:N matching problem). Verification usually involves cooperative subjects in applications such as access control whereas identification applications (e.g., recognizing subjects in surveillance videos) can involve subjects that may or may not be cooperative. An example application of biometric identification is found in some United Arab Emirates (UAE) facilities where access is controlled usFigure 1. Schematic of the use of correlation pattern recognition for biometrics.