Using Fragile Bit Coincidence to Improve Iris Recognition Karen P. Hollingsworth, Kevin W. Bowyer, and Patrick J. Flynn Abstract— The most common iris biometric algorithm repre- sents the texture of an iris using a binary iris code. Not all bits in an iris code are of equal value. A bit is deemed fragile if it varies in value across iris codes created from different images of the same iris. Previous research has shown that iris recognition performance can be improved by masking these fragile bits. Rather than ignoring fragile bits completely, we consider what beneficial information can be obtained from the fragile bits. We find that the locations of fragile bits tend to be consistent across different iris codes of the same eye. We present a metric, called the fragile bit distance, which quantitatively measures the coincidence of the fragile bit patterns in two iris codes. We find that score-fusion of fragile bit distance and Hamming distance works better for recognition than Hamming distance alone. This is the first and only work that we are aware of to use the coincidence of fragile bit locations to improve the accuracy of matches. I. INTRODUCTION Reliable identification of people is required for many applications such as immigration control, aviation security, or safeguarding of financial transactions. Research has demon- strated that the texture of a person’s iris is unique and can be used as a means of identification [1]. Improving the accuracy and reliability of iris recognition is the goal of many current research endeavors [2]. The canonical iris recognition system involves a number of steps [2]. First, a camera acquires an image of an eye. Next, the iris is located within the image. The annular region of the iris is “unwrapped”, or transformed from raw image coordinates to normalized polar coordinates. A texture filter is applied to numerous locations on the iris, and the filter responses are quantized to yield a binary iris code. Finally, the iris code is compared with a known iris code in the gallery, and a similarity or distance score is reported. In an identity-verification application, the system uses the reported score to decide whether the two compared iris codes are from the same subject or from different subjects. A. Fragile Bit Masking Not all bits in the iris code are consistent across different images of the same iris. The concept that some bits in the iris code are less consistent than others was first suggested by Bolle et al. [3]. We can improve the traditional iris recognition system by masking bits of the iris code which are less consistent [4]. The system applies Gabor filters to a number of locations on an iris image and obtains a complex filter response for each location. Each complex filter response is quantized to two bits; the first bit is set to one if the real Karen Hollingsworth, Kevin Bowyer, and Patrick Flynn (kholling, kwb, flynn@cse.nd.edu) are with the University of Notre Dame. part of the complex number is positive, and the second bit is one if the imaginary part is positive. Consider multiple images of the same iris. A filter applied to one location on the iris produces a complex value. Across all images, the complex values for that location will be similar, but not exactly the same. Similarly, the bit from the binary quantization could be the same across all iris codes, or it may differ in some of the codes. A bit in a subject’s iris code is consistent if it assumes the same value for most images of that subject. A bit is fragile if it varies in value some substantial percent of the time. For a filter applied to a specific location in a single image, if the real part of the complex number has a large magnitude, then the corresponding bit will likely be consistent. On the other hand, if the real part is close to zero (or close to the vertical axis of the complex plane), the corresponding bit is fragile. Similar logic applies to the imaginary bits. To illustrate this concept, we took 54 images of the same iris, and for each image, looked at the filter response for one particular spot on the iris. The resulting 54 complex numbers are shown in Figure 1. For this spot on the iris, all of the filter responses had a positive real value, but the imaginary part was positive about half of the time and negative the other half of the time. Therefore, the corresponding real bit in the iris code was consistent, and the corresponding imaginary bit was fragile. This concept suggests a simple optimization to iris recog- nition algorithms. When generating an iris code, we sort the real parts of the complex numbers, and identify a fraction of numbers with the smallest magnitude. Daugman suggested selecting the lowest quartile of values [5]. The corresponding bits in the iris code are masked, or not considered when obtaining distance scores. The bits corresponding to the smallest imaginary values are likewise masked. With this modification, the final score in a comparison is based on fewer bits, but each bit used is more consistent. We call this modification fragile bit masking. For a more in-depth discussion of fragile bits, see our earlier work [4]. B. Motivation of Proposed Method When using fragile bit masking [4], we mask a significant amount of information because it is not “stable”. Rather than completely ignoring all of that fragile bit information, we would like to find a different way to use those bits. We know that the values (zero/one) of those bits are not stable. However, the physical locations of those bits should be stable and might be used to to improve our iris recognition performance.