# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 1 Introduction  
With the advantages of uniqueness, stability, and security of iris texture, iris recognition [1] stands out among numerous biometric recognition technologies [2]. The iris recognition process includes iris image acquisition, iris preprocessing, and iris feature extraction and recognition [3, 4]. Iris segmentation [5] is the accurate location of the iris region in the whole image, which plays a decisive role in the subsequent iris feature expression and recognition rate and is an important step in the entire iris recognition process. Due to the quality differences caused by factors such as heterogeneous spectrum (visible and near-infrared light) and different iris region sizes, the fast and accurate segmentation of multi-source heterogeneous iris images brings new challenges to iris recognition. The main traditional iris segmentation methods are the calculus circle template method proposed by [PERSON] [6] and the Hough method based on edge detection [7]. Many subsequent algorithms have been improved and innovated based on these two methods. Traditional iris segmentation methods can only accurately segment the iris region under ideal conditions. However, conditions are often non-ideal in practice, e.g., due to noise interference from upper and lower eyelid occlusion, eyelash occlusion, and small grayscale changes in the outer boundary of the iris. Traditional iris segmentation methods are susceptible to these noises, leading to a great reduction in localization accuracy and thus affecting the recognition results. With the development of deep learning in recent years, semantic segmentation methods based on deep neutral networks have been widely used in iris segmentation. Semantic segmentation methods at present can be mainly divided into two kinds. One is based on region classification, represented by Mask-RCNN [8]. The working principle is to combine target detection and semantic segmentation. First, the target region is selected through target detection algorithms such as Fast-RCNN [9] and Faster-RCNN [10]. Then, a classifier such as SVM is used to classify the region to perform pixel classification. However, since global semantic information is not considered, segmentation accuracy will decrease in a slightly complex background. Another is semantic segmentation based on end-to-end form, avoiding the problems caused by the generation of the target region. A deep neural network is used to extract semantic information from images with annotation, learn and infer the pixel category of the original images based on the information, and classify each pixel in order to achieve the goal of semantic segmentation. The existing end-to-end iris semantic segmentation methods are mostly based on several typical convolutional neural networks, such as FCN [11], SegNet [12], and U-Net [13]. [PERSON] et al. [14] proposed a fully residual network model FRED-Net, which segmented the true iris boundary without the extra cost of denoising as a preprocessing step. [PERSON] et al. [15] proposed a neural network model, PI-Unet, for iris segmentation to segment heterogeneous iris images. [PERSON] et al. [16] put forward a deep-learning-based iris segmentation method, IrisParseNet. A multi-task, joint approach was adopted to learn iris features in order to solve the noise problem of iris images. [PERSON] et al. [17] proposed a neural network, IRUNet, for robust iris segmentation in a non-cooperative environment to locate the inner and outer iris boundaries, respectively. [PERSON] et al. [18] proposed an iris segmentation method, MFFIris-Unet, for heterogeneous noise, distinguishing noises and iris targets. In summary, the iris segmentation algorithms based on deep learning improve the shortcomings of the traditional iris segmentation algorithms to a certain extent and enhance segmentation accuracy.