Accurate iris segmentation in non-cooperative environments using fully convolutional networks

Liu, Nianfeng; Li, Haiqing; Zhang, Man; Liu, Jing; Sun, Zhenan; Tan, Tieniu

doi:10.1109/icb.2016.7550055

Cited by 151 publications

(111 citation statements)

References 23 publications

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“…Biometric verification systems like iris [125,65], fingerprint [94], finger vein [170], dental records [93], involve segmentation of various informative regions for efficient analysis.…”

Section: Forensicsmentioning

confidence: 99%

Understanding Deep Learning Techniques for Image Segmentation

et al. 2019

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The machine learning community has been overwhelmed by a plethora of deep learning based approaches. Many challenging computer vision tasks such as detection, localization, recognition and segmentation of objects in unconstrained environment are being efficiently addressed by various types of deep neural networks like convolutional neural networks, recurrent networks, adversarial networks, autoencoders and so on. While there have been plenty of analytical studies regarding the object detection or recognition domain, many new deep learning techniques have surfaced with respect to image segmentation techniques. This paper approaches these various deep learning techniques of image segmentation from an analytical perspective. The main goal of this work is to provide an intuitive understanding of the major techniques that has made significant contribution to the image segmentation domain. Starting from some of the traditional image segmentation approaches, the paper progresses describing the effect deep learning had on the image segmentation domain. Thereafter, most of the major segmentation algorithms have been logically categorized with paragraphs dedicated to their unique contribution. With an ample amount of intuitive explanations, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.

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“…Biometric verification systems like iris [125,65], fingerprint [94], finger vein [170], dental records [93], involve segmentation of various informative regions for efficient analysis.…”

Section: Forensicsmentioning

confidence: 99%

Understanding Deep Learning Techniques for Image Segmentation

et al. 2019

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“…Sahmoud and Abuhaiba (Sahmoud and Abuhaiba, 2013) have used K-means clustering and circular HT and Tan and Kumar (Tan and Kumar, 2014) have proposed Zernike moments to extract the iris pixel information. Recently, Gangwar et al (Gangwar et al, 2016) have proposed a boundary based course-to-fine strategy for iris localization and Liu et al (Liu et al, 2016) have proposed iris segmentation models using convolutional neural network. We have proposed a segmentation strategy which exploits both, the pixel information and position with respect to the pixels in the neighbourhood and the geometry of the iris region.…”

Section: Related Workmentioning

confidence: 99%

“…The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W4, 2017 2nd International ISPRS Workshop on PSBB, 15-17 May 2017, Moscow, Russia based on the edge information (boundary based methods) and the second category segments the candidate iris region based on the pixel information (pixel-based methods) (Liu et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

“…Pixel based methods have used the information such as color, intensity variation and texture of the iris to extract the discriminative appearance feature of a pixel from the neighbourhood pixels (Liu et al, 2016). Proenca and Alexandre (Proença and Alexandre, 2006) have introduced unsupervised clustering techniques, Jeong et al (Jeong et al, 2010) have used Adaboost eye detection and color segmentation, intelligent random sample consensus iris segmentation on four spectral images is proposed by Chou et al (Chou et al, 2010) and clustering based coarse eye localization and integro-differential constellation are used by Tan et al (Tan et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

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Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Shekar

Bhat²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

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“…Due to its lower complexity this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets. Liu,in [72] proposed two CNN approaches to segment noisy iris images acquired under unconstrained conditions. In the first approach called hierarchical convolutional neural networks (HCNNs), three patches taken from different scales of the same image are used as input.…”

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confidence: 99%

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

2020

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A data augmentation methodology is presented and applied to generate a large dataset of offaxis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets. Liu,in [72] proposed two CNN approaches to segment noisy iris images acquired under unconstrained conditions. In the first approach called hierarchical convolutional neural networks (HCNNs), three patches taken from different scales of the same image are used as input. The HCNN consists of three similar blocks, a combination of convolutional and pooling layers that are merged together into a fully connected layer. In the second approach, 31 convolutional layers and 6 pooling layers are used to compose the multi-scale fully convolutional network (MFCNs). Both models are end-to-end, with no requirement for pre-or post-processing of the image. Arsalan [73], introduced a two-stage iris segmentation method. The first stage includes a pre-processing of the image and the use of a modified Hough Transform to identify the region of interest (ROI). In the second stage, a mask of [21 × 21] pixels, based on the ROI defined in the previous stage, is fed to a pre-trained VGG-face model which classifies the pixels as iris or non-iris. In a follow up work which is focused on segmenting low quality iris images, Arsalan in [74], proposed a densely connected fully convolutional network (IrisDenseNet), consisting of two main components: a densely connected encoder and a SegNet decoder. In a similar work, Bazrafkan in [43], presented a network design focused on segmenting iris of inferior quality. Four different end-to-end fully convolutional networks are merged into a single model using a method known as Semi Parallel Deep Neural Networks (SPDNN). In this way, the final model benefits from each of the four distinct network designs. Finally, since the existence of a large labelled dataset is a prerequisite in order to implement a convolutional neural network approach, Jalilian in [75] to overcome this obstacle, introduced a domain adaption method so that a CNN for iris segmentation could be trained with a limited data. ContributionsThe focus of this work is to improve the segmentation of off-axis iris images originating from the unconstrained conditions of a user-facing camera on wearable AR/VR device.The model proposed is an end to end deep neural network which accepts an off-axis eye-region image and generates the corresponding binary segmentation map for the iris region as output. Performance evaluation of the proposed model shows advantages over recent iris segmentation tec...

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Accurate iris segmentation in non-cooperative environments using fully convolutional networks

Cited by 151 publications

References 23 publications

Understanding Deep Learning Techniques for Image Segmentation

Understanding Deep Learning Techniques for Image Segmentation

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

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