Iris matching for rapid identification with configurable hardware

Iris recognition (IR) is one of the most accurate biometric identification methods available, with custom algorithms deployed globally in a variety of systems ranging from personal computers to portable scanners. The overall performance of these systems—based on their size, shape, speed, power, and accuracy—is of considerable interest to commercial and military vendors alike. As IR systems increase in popularity, the new algorithms are revealing their capabilities in greater detail. Recently, we developed the ridge energy direction (RED) algorithm,1 to improve performance for the end user. The iris, an internal part of the eye protected by the cornea, sustains its appearance over decades. After an image of the iris is digitally captured (see Figure 1), the image is transformed from a 2D array of pixels into a 2D encoded string of bits for comparison, a process referred to as templating (see Figure 1, upper left).2 The IR system must reliably match the new template with one previously enrolled. The newly encoded iris is compared to a database using a fractional hamming distance (HD) calculation, defined as follows: