# From Iris Image to Embedded Code: System of Methods
## 1 Introduction  
Nowadays, cryptographic algorithms are widely used for information protection. A large number of them, as well as their applications, have been invented and introduced [1]. These algorithms and systems are mathematically grounded and reliable. The weak link in their implementation and usage, as usual, is human. Cryptography requires keys, i.e., sequences of digits, which should be reproduced precisely. While a human is able to remember and reproduce a personally invented password (though there are difficulties here already), it is practically impossible to memorize a long sequence of pseudorandom symbols, which is created automatically [2]. Meanwhile, humans possess biometric features that are simple to retrieve, difficult to alienate, and contain a significant amount of information. The disadvantage of biometric traits is their variability: it is impossible to exactly replicate the measurement results, we can only say that two sets of traits taken from one person are in some sense closer than the sets obtained from different people. It is of great interest to combine these two approaches, i.e., to develop methods for obtaining unique cryptographic keys from variable biometry data of a given person. The eye iris is the most suitable biometric modality among all non-invasive ones due to its highest information capacity. The number of degrees of freedom of the iris template was evaluated as 249 [3]. It promises to be almost as good as a strong symmetric cryptography key length of 256 bit, while the net coming fingerprint is reported to have 80 bits [4]. In order to design a practically usable system it is advisable to base it on the iris. Up to now a major focus in developing automated biometric is building an identification system, i.e., the system, which executes a scenario: sample biometric features once, record, take them sometime later and decide whether these samples belong to the same person. The workflow of the biometric identification system can be combined of the blocks: capture, segmentation, template generation, and template matching, see Figure 1.  
Note that in this scenario the biometric template should be securely stored and exclude the intruder from obtaining it. Here, a different problem is solved, thus only capture, segmentation and partly template generation blocks are inherited, and matching is replaced by embedding/extracting the cryptographic key into/from the biometric features. The explanation here goes alongside the data processing: from the source iris image to the embedding of the secret key. The capture process, i.e., obtaining eye images with a camera device, is beyond the scope of this paper. We start from the image segmentation task and present a framework for locating the iris in an eye image. In the next section clue methods of the framework are described. Then feature extraction and matching methods are given. Following is the discussion of the application scenario of embedding the secret key to biometric features. The successful extraction of the embedded key depends on the difference between registered and presented biometric features, the value of this difference is determined based on several databases. In the next section the methods of encoding and decoding the key are presented, and the selection of their optimal parameters is discussed. The contribution of this work is comprised of the following. * The system of iris segmentation methods is presented which combines preliminary detection with refinement steps. The first steps use the most common geometric properties of the eye and accept the widest range of image conditions, while the final steps take care of details. The core of the system is a special base radii detection method. * The cascade of error correction codecs adopted to iris code nature. A novel step of pseudorandom bit shuffling is introduced, accompanied by bit dubbing. This contradicts known methods, which do not use bit dubbing and deliberately avoid bit shuffling.