Correlation pattern recognition for biometrics


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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.
Many biometric modalities produce images, e.g., face, fingerprints, and iris. The goal is to match the live biometric images to those collected during enrollment. The major challenge is that biometric patterns often exhibit significant inter-class variability (e.g., the appearance of face images is affected by
pose, illumination and expression) and inadequate differences
between various subjects.
Well-designed biometric verification systems therefore attempt
to decrease both the false accept rate (FAR) i.e., the rate at which
impostors are incorrectly accepted as authentic, and the false
reject rate (FRR) i.e., the rate at which authentic users are incorrectly
rejected as impostors.
Most biometric recognition approaches use features computed from the images. The choice of features is critical to the efficiency of recognition systems and many types of features (e.g., Gabor wavelet-based features for iris images,minutiae for fingerprints, etc.) have been investigated. The resulting feature vector selected as the test biometric is compared to the stored feature vector (also called a template) to obtain a similarity value between them. If the similarity is above a chosen threshold, a match is declared and a no match is declared otherwise. By changing the threshold, FAR and FRR values can be adjusted.