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Iris and periocular recognition in arabian race horses using deep convolutional neural networks

Mateusz Trokielewicz, Mateusz Szadkowski
2017 2017 IEEE International Joint Conference on Biometrics (IJCB)  

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2018 pre-print arXiv:1809.00213v1 fatcat:txlhq6ehcrf3pdneuk4opro334
This paper presents a study devoted to recognizing horses by means of their iris and periocular features using deep convolutional neural networks (DCNNs).  ...  In our work, we examine a possibility of utilizing deep convolutional neural networks for a fusion of both iris and periocular region features.  ...  Compared with iris recognition, periocular recognition often requires less constrained image acquisition conditions, such as imaging in visible light, ata-distance, or on-the-move.  ... 
doi:10.1109/btas.2017.8272736 dblp:conf/icb/TrokielewiczS17 fatcat:ls73ed35wvfw7kiyni7xtc5g6i
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Mateusz Trokielewicz, Mateusz Szadkowski. "Iris and periocular recognition in arabian race horses using deep convolutional neural networks." 2017 IEEE International Joint Conference on Biometrics (IJCB) (2017) 510-516

Towards Accurate and Lightweight Masked Face Recognition: an Experimental Evaluation

Yoanna Martinez-Diaz, Heydi Mendez-Vazquez, Luis S. Luevano, Miguel Nicolas-Diaz, Leonardo Chang, Miguel Gonzalez-Mendoza
2021 IEEE Access  

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The semantics-assisted convolutional neural networks (SCNN) [44] was one of the first proposals that use deep learning-based representation for periocular images.  ...  With the emergence of deep learning approach, the focus of the researchers has been moved to learn robust representations by deep Convolutional Neural Networks (CNNs) for periocular recognition, achieving  ...  Moreover, although masked-based models allow us to obtain higher recognition performance than periocular-based models, we think that combining both approaches could improve the performance of current masked  ... 
doi:10.1109/access.2021.3135255 fatcat:4my36yltffdbzaf7u46ghmnw2q
DOAJ
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Yoanna Martinez-Diaz, Heydi Mendez-Vazquez, Luis S. Luevano, Miguel Nicolas-Diaz, Leonardo Chang, Miguel Gonzalez-Mendoza. "Towards Accurate and Lightweight Masked Face Recognition: an Experimental Evaluation." IEEE Access (2021) 1-1

Deep Learning for Iris Recognition: A Survey [article]

Kien Nguyen, Hugo Proença, Fernando Alonso-Fernandez
2022 arXiv   pre-print

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Fourth, we review open-source resources and tools in deep learning techniques for iris recognition.  ...  Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition.  ...  The work due to Hugo Proença was funded by FCT/MEC through national funds and co-funded by FEDER -PT2020 partnership agreement under the projects UIDB/50008/2020, POCI-01-0247-FEDER-033395.  ... 
arXiv:2210.05866v1 fatcat:7xhng57jhrdibi6vuvb2pqxdni
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Citation

Kien Nguyen, Hugo Proença, Fernando Alonso-Fernandez. "Deep Learning for Iris Recognition: A Survey." arXiv (2022)

Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Mahmut Karakaya
2021 Journal of Electronic Imaging (JEI)  

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We present convolutional neural network (CNN)-based deep learning frameworks to improve the recognition performance of iris, ocular, and periocular biometric modalities for off-angle images.  ...  Standoff biometric systems require a less controlled environment than traditional systems, so their captured images will likely be nonideal, including off-angle.  ...  Acknowledgments This project was made possible by support from the SaTC program of NSF under grant awards CNS-1909276 and CNS-2100483.  ... 
doi:10.1117/1.jei.30.3.033035 fatcat:kf2ecktdknatvn7iuw3xwnxaga
Citation

Mahmut Karakaya. "Iris-ocular-periocular: toward more accurate biometrics for off-angle images." Journal of Electronic Imaging (JEI) 30.03 (2021)

Iris Recognition Development Techniques: A Comprehensive Review

Jasem Rahman Malgheet, Noridayu Bt Manshor, Lilly Suriani Affendey, Alfian Bin Abdul Halin, Rosa M. Lopez Gutierrez
2021 Complexity  

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Notwithstanding this, there are several challenges in unrestricted recognition environments.  ...  In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase.  ...  Some of the iris images were acquired under a constrained (ideal) environment, while other images were acquired under an unconstrained (non-ideal) environment.  ... 
doi:10.1155/2021/6641247 fatcat:xil2fsokf5eetc2cx4br7bcquy
DOAJ Szczepanski
Citation

Jasem Rahman Malgheet, Noridayu Bt Manshor, Lilly Suriani Affendey, Alfian Bin Abdul Halin, Rosa M. Lopez Gutierrez. "Iris Recognition Development Techniques: A Comprehensive Review." Complexity (2021) 1-32

Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models

Fadi Boutros, Naser Damer, Kiran Raja, Florian Kirchbuchner, Arjan Kuijper
2022 Sensors  

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This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters.  ...  Our results demonstrate the superiority of our proposed approach over a network trained without KD and networks trained with conventional (vanilla) KD.  ...  Zhao and Kumar [33] proposed a semantics-assisted CNN (SCNN) model consisting of multiple CNN models for periocular recognition. Rattani et al.  ... 
doi:10.3390/s22051921 pmid:35271074 pmcid:PMC8914924 fatcat:j5zvmpmzgrdzrdxc3jt2x3zuru
DOAJ
Citation

Fadi Boutros, Naser Damer, Kiran Raja, Florian Kirchbuchner, Arjan Kuijper. "Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models." Sensors 22.5 (2022) 1921

Semantic Segmentation of the Eye with a Lightweight Deep Network and Shape Correction

Van Thong Huynh, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim
2020 IEEE Access  

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The deep network is built upon an encoder-decoder scheme with depthwise separation convolution for the low-resource systems.  ...  Our system can run on the resource-constrained environments, such as mobile, embedded devices for real-time inference, while still ensuring the accuracy.  ...  DEPTHWISE SEPARABLE CONVOLUTIONS Depthwise separable convolutions are a core component of most efficient deep neural networks that work in mobile and other resource-constrained environments [23] , [32  ... 
doi:10.1109/access.2020.3010011 fatcat:xomhifaluvbgjafwg2cups7wfe
DOAJ
Citation

Van Thong Huynh, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim. "Semantic Segmentation of the Eye with a Lightweight Deep Network and Shape Correction." IEEE Access (2020) 1-1

Deep Learning for Iris Recognition: A Review [article]

Yimin Yin, Siliang He, Renye Zhang, Hongli Chang, Xu Han, Jinghua Zhang
2023 arXiv   pre-print

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We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition.  ...  With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition  ...  The periocular-assisted iris identification In less constrained imaging environments, iris images present different regions of effective iris pixels.  ... 
arXiv:2303.08514v1 fatcat:ujifadxws5bajcwtskp5qumpxe
Citation

Yimin Yin, Siliang He, Renye Zhang, Hongli Chang, Xu Han, Jinghua Zhang. "Deep Learning for Iris Recognition: A Review." arXiv (2023)

The Previous Academic Papers and Publications and Turnitin Similarity Report, 63 pages. (Siming Zheng_ca)

Siming Zheng
2020 figshare.com  

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It provided the Turnitin Similarity report for the second paper, which was under the peer-review after 2nd revision.  ...  Rahmat for assistance with data collection, providing helpful discussions of the 3D face analyses.  ...  I thank Fatimah Khalid for her help in the depth analysis and comments on the face recognition theory, as well as Prof. Dr. Nurul Amelina Nasharuddin for fruitful advices.  ... 
doi:10.6084/m9.figshare.13298981.v1 fatcat:fkw4ipxwozbtjjeqiqmaseiyuu
Citation

Siming Zheng. "The Previous Academic Papers and Publications and Turnitin Similarity Report, 63 pages. (Siming Zheng_ca)." figshare.com (2020)

The Previous Academic Papers and Publications and Turnitin Similarity Report, 63 pages. (Siming Zheng)

Siming Zheng
2020 figshare.com  

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It provided the Turnitin Similarity report for the second paper, which was under the peer-review after 2nd revision.  ...  Rahmat for assistance with data collection, providing helpful discussions of the 3D face analyses.  ...  I thank Fatimah Khalid for her help in the depth analysis and comments on the face recognition theory, as well as Prof. Dr. Nurul Amelina Nasharuddin for fruitful advices.  ... 
doi:10.6084/m9.figshare.13271087.v1 fatcat:vnn37jx7njb6lhxknz6dtxchpy
Citation

Siming Zheng. "The Previous Academic Papers and Publications and Turnitin Similarity Report, 63 pages. (Siming Zheng)." figshare.com (2020)

The Previous Academic Papers and Publications and Turnitin Similarity Report for HKU, 63 pages. (Siming Zheng)

Siming Zheng
2020 figshare.com  

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It provided the Turnitin Similarity report for the second paper, which was under the peer-review after 2nd revision.  ...  Rahmat for assistance with data collection, providing helpful discussions of the 3D face analyses.  ...  I thank Fatimah Khalid for her help in the depth analysis and comments on the face recognition theory, as well as Prof. Dr. Nurul Amelina Nasharuddin for fruitful advices.  ... 
doi:10.6084/m9.figshare.13302287.v1 fatcat:yiegrymhzrgrff6lnyscr4bfr4
Citation

Siming Zheng. "The Previous Academic Papers and Publications and Turnitin Similarity Report for HKU, 63 pages. (Siming Zheng)." figshare.com (2020)

Phase-Based Periocular Recognition with Texture Enhancement

Luis Rafael MARVAL-PÉREZ, Koichi ITO, Takafumi AOKI
2019 IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences  

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We address this problem by applying texture enhancement and found out that variance normalization of texture significantly improves the performance of periocular recognition using PB-CM.  ...  Access control and surveillance applications like walkingthrough security gates and immigration control points have a great demand for convenient and accurate biometric recognition in unconstrained scenarios  ...  Among recent periocular recognition methods, we find those based on Convolutional Neural Networks (CNNs) [23] - [25] and those based on a correlation filter known as Fusion Optimal-Trade-off Synthetic  ... 
doi:10.1587/transfun.e102.a.1351 fatcat:zbyssjki65ayna2e2x66oucfle
Citation

Luis Rafael MARVAL-PÉREZ, Koichi ITO, Takafumi AOKI. "Phase-Based Periocular Recognition with Texture Enhancement." IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E102.A.10 (2019) 1351-1363

Ocular biometrics: A survey of modalities and fusion approaches

Ishan Nigam, Mayank Vatsa, Richa Singh
2015 Information Fusion  

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Iris recognition is used in Unique Identification Authority of India's Aadhaar Program and the United Arab Emirate's border security programs, whereas the periocular recognition is used to augment the  ...  machine learning algorithms for better representation and classification, (iv) developing algorithms for ocular recognition at a distance, (v) using multimodal ocular biometrics for recognition, and (  ...  Tan and Kumar [91] propose a strategy for accurate iris recognition from distantly acquired face or eye images under less constrained environments.  ... 
doi:10.1016/j.inffus.2015.03.005 fatcat:ph2katoyuzdylamlesnt7vzbay
Citation

Ishan Nigam, Mayank Vatsa, Richa Singh. "Ocular biometrics: A survey of modalities and fusion approaches." Information Fusion (2015) 1-35

Deep Learning in Diverse Intelligent Sensor Based Systems

Yanming Zhu, Min Wang, Xuefei Yin, Jue Zhang, Erik Meijering, Jiankun Hu
2022 Sensors  

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Convolutional Neural Network (CNN) 3.1.1.  ...  Deep learning has been widely used in assisting this.  ... 
doi:10.3390/s23010062 pmid:36616657 pmcid:PMC9823653 fatcat:riifuhqtnrbrrkat26mxummwd4
DOAJ
Citation

Yanming Zhu, Min Wang, Xuefei Yin, Jue Zhang, Erik Meijering, Jiankun Hu. "Deep Learning in Diverse Intelligent Sensor Based Systems." Sensors 23.1 (2022) 62

Deep Learning in Information Security [article]

Stefan Thaler, Vlado Menkovski, Milan Petkovic
2018 arXiv   pre-print

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Deep Learning is a sub-field of machine learning, which uses models that are composed of multiple layers.  ...  Consequently, representations that are used to solve a task are learned from the data instead of being manually designed.  ...  Accurate Periocular Recognition under Less Constrained Environment Using Semantics-Assisted Convolutional Neural Network.  ... 
arXiv:1809.04332v1 fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
Citation

Stefan Thaler, Vlado Menkovski, Milan Petkovic. "Deep Learning in Information Security." arXiv (2018)

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