# From Iris Image to Embedded Code: System of Methods
## 2 Eye Segmentation Framework  
Methods, algorithms and applications of iris biometrics have attracted much attention during the last three decades [5; 6; 7] and continue developing rapidly in recent years [8]. The main trend of the latest development is switching from heuristic approaches and hand-crafted methods to employing neural networks in various tasks. A wide variety of artificial neural networks has emerged and is applied to iris segmentation, starting from earlier research with fully connected nets [9] to latest applications of attention-driven CNNs [10], U-Nets [11], hybrid deep learning [12]. Another trend comes from the in-born ability of neural networks to _classify_ objects (say, pixels, textures, images) rather than _calculate_ their positions and other numerical properties. Due to this, most of the works in iris segmentation rely on detecting masks, i.e., pixels belonging to regions of the iris (or pupil, or else) in the image. Positions and sizes of pupil and iris are then derived from these masks. Surely, detecting masks is what one calls segmentation; however, such an approach ignores the clear and simple geometry of the iris and is prone to detecting irises of  
Figure 1: Biometric system workflow. unnatural shape as is shown in [12]. Some works [13; 14] apply a neural network to detect the position of the iris as a number; however, it seems a strained decision. Here we adopt a \"classical\" method. The obvious approach to iris region extraction in eye imaging is a chain of operations that starts with the detection of the pupil (the most distinctive area that is dark and has an expressed circular shape). Then outer iris border is presumably located. Finally, the visible iris part is refined by cutting off the areas distorted by reflections, eyelids and eyelashes. Most researchers and developers follow this method. Detection of each iris feature is usually carried out only one time and it is not recalculated any more even after obtaining other features, which can serve for refinement. For instance, in [15; 16; 17; 18] full set of iris features is detected; however, pupil parameters are obtained at the first step and are not revised any more. Only a few papers develop something different from this sequence \"first pupil, then iris, once determined, and never reviewed\". In [19; 20], the position of the iris center is estimated first which makes pupil detection more robust. In [21], pupil parameters are refined using iris size after the iris is located. In [21; 22], detection methods run iteratively several times for refinement. In [20; 23], a multi-scale approach is used, and methods run in several scales. However, none of these works use various types of methods for detecting any iris feature. Here we develop a _system of methods_ for segmentation of the iris in an eye image. Evaluating of each of parameters is performed at several steps. The main idea of this system is that at first the most general characteristics of objects are defined, which are then sequentially supplemented by more specific and refined ones. Beginning steps do not need to output precise final parameters, used as final. Instead, they should be robust and general and tolerate a wide range of conditions, i.e., detect the object of any quality. Later steps should have the highest possible precision and may reject poor quality data. Iris region in frontal images is delimited by two nearly concentric nearly circular contours, called inner and outer borders. Hereinafter the contour separating iris and pupil is referred to as _inner border_, _pupil border_ or simply _pupil_, and the contour delimiting iris and sclera is called _outer border_ or _iris_. In most cases pupil border is wholly visible in the image, but some part of the iris border is frequently overlapped by eyelids and eyelashes. Since the pupil and iris are almost concentric, one _eye center_ point may serve as an approximate center for both contours.