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The Iris architecture and implementation

K. Wilkinson, P. Lyngbaek, W. Hasan
1990 IEEE Transactions on Knowledge and Data Engineering  
The authors wish to thank Jurgen Annevelink, Dan Fishman, Stefan Gower, Marie-Anne Neimat, Emmanuel" Onuegbe, Katie Rotzell and the reviewers for helpful comments on this paper.  ...  Iris Kernel Architecture Overview The Iris Kernel is a program that implements the Iris data model.  ...  The emphasis of this paper is to describe the Iris Kernel architecture. Section 2 gives a brief overview of the Iris Data Model. Section 3 describes the Iris Kernel architecture.  ... 
doi:10.1109/69.50906 fatcat:glwmanaq7nhw3lbnnr7c4al72u

Iris Recognition using Convolutional Neural Network Design

Iris trait has gained the attention of many researchers recently as it consists of unique and highly random patterns.  ...  The effect of various optimization techniques for generalization ability is also observed. The method is tested on IITD and CASIA-Iris-V3 database.  ...  This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  ... 
doi:10.35940/ijitee.i1108.0789s19 fatcat:uigwkrdgfbfhlohervrcrfxidq

Iris matching algorithm on many-core platforms

Chen Liu, Benjamin Petroski, Guthrie Cordone, Gildo Torres, Stephanie Schuckers
2015 2015 IEEE International Symposium on Technologies for Homeland Security (HST)  
computation onto high-performance many-core architectures.  ...  The results show the ability of the iris matching application to efficiently scale and fully exploit the capabilities offered by many-core platforms and provide insights in how to migrate the biometrics  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1109/ths.2015.7225264 fatcat:d7w7jsezvrednae3tc5eo5dk44

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

Guoyang Liu, Weidong Zhou, Lan Tian, Wei Liu, Yingjian Liu, Hanwen Xu
2021 Sensors  
We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.  ...  In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios.  ...  Acknowledgments: We gratefully acknowledge the support from the above funds. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21113721 pmid:34071850 fatcat:wtboadxr6jgd7gjcl3idkj3dyq

Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data [article]

Manashi Chakraborty, Mayukh Roy, Prabir Kumar Biswas, Pabitra Mitra
2020 arXiv   pre-print
This drives the network weights to focus on discriminative iris texture patterns.  ...  Firstly, to address the dearth of labelled iris data, we propose a reconstruction loss guided unsupervised pre-training stage followed by supervised refinement.  ...  This can be primarily attributed to the fact that the kernels of VGG-16 were trained to learn structure and shape cues present in natural images and not texture-rich contents as prevalent in iris images  ... 
arXiv:2002.09048v1 fatcat:qsvbmrxdtfgipdmhi73ymfxjqq

Iris nevus diagnosis: convolutional neural network and deep belief network

2017 Turkish Journal of Electrical Engineering and Computer Sciences  
Iris nevus is a pigmented growth (tumor) found in the front of the eye or around the pupil.  ...  It is seen that racial and environmental factors affect the iris color (e.g., blue, hazel, brown) of patients; hence, pigmented growths may be masked in the eye background or iris.  ...  The diagnosis of iris nevus is made difficult in that patients often have different colors of irises; hence, the pigmented growths may be concealed within the iris when manual examination of medical eye  ... 
doi:10.3906/elk-1507-190 fatcat:mcyh7elxrzcnzhzxu2xxgihqca

Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification [article]

Sobhan Soleymani, Ali Dabouei, Hadi Kazemi, Jeremy Dawson, Nasser M. Nasrabadi
2018 arXiv   pre-print
In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification.  ...  We also demonstrate that the joint optimization of all the modality-specific CNNs excels the score and decision level fusions of independently optimized CNNs.  ...  ACKNOWLEDGEMENT This work is based upon a work supported by the Center for Identification Technology Research and the National Science Foundation under Grant #1650474.  ... 
arXiv:1807.01332v1 fatcat:eyjp3abfcje6hdltrvcaidzzji

Multi-source feature fusion and entropy feature lightweight neural network for constrained multi-state heterogeneous iris recognition

Liu Shuai, Liu Yuanning, Zhu Xiaodong, Huo Guang, Cui Jingwei, Zhang Qixian, Wu Zukang, Li Xinlong, Wang Chaoqun
2020 IEEE Access  
As the requirement for the number and quality of irises changes, the category labels in the recognition function are dynamically adjusted using a feedback learning mechanism.  ...  The information entropy of the iris feature label is used to set the iris entropy feature category label and design the recognition function according to the category label to obtain the recognition result  ...  ACKNOWLEDGMENT The authors would like to thank the referee's advice.  ... 
doi:10.1109/access.2020.2981555 fatcat:z5yd6eyolfbufhw3sky6idtzza

Heterogeneous Iris One-to-One Certification with Universal Sensors based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

Liu Shuai, Liu Yuanning, Zhu Xiaodong, Huo Guang, Wu Zukang, Li Xinlong, Wang Chaoqun, Cui Jingwei
2020 Sensors  
The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.  ...  As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20061785 pmid:32210211 pmcid:PMC7146378 fatcat:vussjtjqf5gnzauhook5kqalba

Eye Semantic Segmentation with a Lightweight Model [article]

Van Thong Huynh, Soo-Hyung Kim, Guee-Sang Lee, Hyung-Jeong Yang
2019 arXiv   pre-print
Our model based on the encoder decoder structure with the key is the depthwise convolution operation to reduce the computation cost.  ...  We achieved the mean intersection over union (mIoU) of 94.85% with a model of size 0.4 megabytes. The source code are available  ...  The encoder architecture of our method.  ... 
arXiv:1911.01049v1 fatcat:zcfpsk4cz5a2fm7vieqhcy4vnm

Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes [article]

Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz
2019 arXiv   pre-print
, iris-specific kernels learnt by Siamese networks.  ...  The resulting method significantly outperforms the existing off-the-shelf iris recognition methods (both academic and commercial) on the newly collected database of post-mortem iris images and for all  ...  Model architecture and filter learning For learning the iris-specific filters we use a shallow Siamese architecture composed of two branches, each responsible for encoding one image from the image pair  ... 
arXiv:1912.02512v1 fatcat:dmdat7ygyjbefpjnpyo56yfuny

Recognition, Classification for Normal, Contact and Cosmetic Iris Images using Deep Learning

2019 International journal of recent technology and engineering  
The identification of the human being using iris based on image processing technique was one of better and older approach in human identification.  ...  The recent technology is enhancing for iris recognition based on deep learning networks, in which deeply train the images with number of layers, so that necessary features are extracted and then classify  ...  Sum of product of kernel value and the input pixel value and the output is considered as kernel Centre.  ... 
doi:10.35940/ijrte.c5185.098319 fatcat:klcefc7b6ncdvehjng7a4mxpam

Complex-valued Iris Recognition Network [article]

Kien Nguyen, Clinton Fookes, Sridha Sridharan, Arun Ross
2022 arXiv   pre-print
We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition.  ...  from the input iris texture in order to better represent its biometric content.  ...  The kernel set has C 2 complex-valued kernels, each with a size of W 3 × H 3 × (2C 1 ).  ... 
arXiv:2011.11198v4 fatcat:yq3why462zawjn4g3fpqx2htdm

Multi-Kernel Fusion for RBF Neural Networks [article]

Syed Muhammad Atif, Shujaat Khan, Imran Naseem, Roberto Togneri, Mohammed Bennamoun
2020 arXiv   pre-print
A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks.  ...  In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels.  ...  All authors discussed the results and approved the final manuscript.  ... 
arXiv:2007.02592v1 fatcat:eulpmiv5kbax3g5hzbbj2ualni

Are Gabor Kernels Optimal for Iris Recognition? [article]

Aidan Boyd, Adam Czajka, Kevin Bowyer
2020 arXiv   pre-print
In this work we investigate, given the current interest in neural networks, if Gabor kernels are the only family of functions performing best in iris recognition, or if better filters can be learned directly  ...  These lead us to two conclusions: (a) a family of functions offering optimal performance in iris recognition is wider than Gabor kernels, and (b) we probably hit the maximum performance for an iris coding  ...  Network Architecture One Convolutional Layer: For this work the network architecture was designed to implement a Daugman-style approach to iris recognition, as shown in Figure 3 .  ... 
arXiv:2002.08959v1 fatcat:frrssua4zzbdlh7kbbpr5ppzgu
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