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Noisy Iris Recognition Based on Deep Neural Network

Eman M. Omran, Randa F. Soliman, Maryam Mostafa Salah, Sameh A. Napoleon, El-Sayed M. El-Rabaie, Mustafa M. AbdeElnaby, Nabil A. Ismail, Ayman A. Eisa, Fathi abd El-samie
2020 Menoufia Journal of Electronic Engineering Research  
Simulation results reveal that using the deep learning greatly improves iris recognition accuracy for Alex CNN. We achieve 100%, 100%, 88.9% for interval, lamp and twins datasets respectively.  ...  Iris recognition is one of the Biometric systems used for persons identification based on their special iris traits, which are unique featuresfor each individual.  ...  Convolutional neural network (CNN) is one of deep learning methods, which designed for purpose of image and video processing.  ... 
doi:10.21608/mjeer.2020.103276 fatcat:zifmyobppvhgpejwinvirbjjrm

Biometrics Recognition Using Deep Learning: A Survey [article]

Shervin Minaee, Amirali Abdolrashidi, Hang Su, Mohammed Bennamoun, David Zhang
2021 arXiv   pre-print
We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks.  ...  In this work, we provide a comprehensive survey of more than 120 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which  ...  Nalini Ratha for reviewing this work, and providing very helpful comments and suggestions.  ... 
arXiv:1912.00271v3 fatcat:nobon7vrrrdnxe4pr3q2anl63y

Down Syndrome Face Recognition: A Review

Olalekan Agbolade, Azree Nazri, Razali Yaakob, Abdul Azim Ghani, Yoke Kqueen Cheah
2020 Symmetry  
Consequently, the use of machine learning methods has facilitated the recognition of facial dysmorphic features associated with DS.  ...  One of the most pertinent applications of image analysis is face recognition and one of the most common genetic disorders is Down syndrome (DS), which is caused by chromosome abnormalities in humans.  ...  The authors manually defined 17 clinically relevant facial anatomical points, which covered most of the inner facial features such as the mouth, eyes, and nose.  ... 
doi:10.3390/sym12071182 fatcat:qgau3gm6pzdapd6wkuyvgg7umy

A new distance measure for non-identical data with application to image classification

Muthukaruppan Swaminathan, Pankaj Kumar Yadav, Obdulio Piloto, Tobias Sjöblom, Ian Cheong
2017 Pattern Recognition  
PBR's performance was evaluated on twelve benchmark data sets covering six different classification and recognition applications: texture, material, leaf, scene, ear biometrics and category-level image  ...  for different distributions in distance measures can improve performance in classification and recognition tasks.  ...  Acknowledgments The authors thank Temasek Life Sciences Laboratory for funding this work and Entopsis LLC (Miami, FL) for providing all computer hardware used in this study.  ... 
doi:10.1016/j.patcog.2016.10.018 fatcat:o4jalrfayrf25fb46augimgozm

Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte, Jörn Diedrichsen
2014 PLoS Computational Biology  
SIFT, GIST, self-similarity features, and a deep convolutional neural network).  ...  Overall, our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in  ...  Please note that one weight is learned for each layer, and each of the SVM discriminants (8 + 3 = 11 weights in total).  ... 
doi:10.1371/journal.pcbi.1003915 pmid:25375136 pmcid:PMC4222664 fatcat:pqkssyaw3fgjfldgiwinxqbro4

Harnessing Synthesized Abstraction Images to Improve Facial Attribute Recognition

Keke He, Yanwei Fu, Wuhao Zhang, Chengjie Wang, Yu-Gang Jiang, Feiyue Huang, Xiangyang Xue
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Different from most previous approaches which predict attributes only based on the whole images, this paper leverages facial parts locations for better attribute prediction.  ...  Then we build a dual-path facial attribute recognition network to utilize features from the original face images and facial abstraction images.  ...  Acknowledgments The authors would like to thank anonymous reviewers for their helpful comments. The authors are also grateful for valuable suggestions from Ying Tai and Yanhao Ge.  ... 
doi:10.24963/ijcai.2018/102 dblp:conf/ijcai/HeFZWJHX18 fatcat:rxb4ooeghzgqhbefgxynv3y2ii

MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification [article]

Xiaoni Li, Yucan Zhou, Yu Zhou, Weiping Wang
2021 arXiv   pre-print
Besides the effect of multiple label structures, we also explore the architecture of the deep model for better hierachical classification and adjust the hierarchical evaluation metrics for multiple label  ...  In this paper, we consider that different label structures provide a variety of prior knowledge for category recognition, thus fusing them is helpful to achieve better hierarchical classification results  ...  For deep hierarchical classification, Frome et al.  ... 
arXiv:2107.00808v1 fatcat:2trbaaf7nrenfh6hqe6xbuug5q

Fine-Grained Image Analysis with Deep Learning: A Survey [article]

Xiu-Shen Wei and Yi-Zhe Song and Oisin Mac Aodha and Jianxin Wu and Yuxin Peng and Jinhui Tang and Jian Yang and Serge Belongie
2021 arXiv   pre-print
Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA.  ...  Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications.  ...  ACKNOWLEDGMENTS The authors would like to thank the editor and the anonymous reviewers for their constructive comments.  ... 
arXiv:2111.06119v2 fatcat:ninawxsjtnf4lndtqquuwl3weq

Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning [article]

Dong Li, Melissa Zavaglia, Guangyu Wang, Yi Hu, Hong Xie, Rene Werner, Ji-Song Guan, Claus C. Hilgetag
2018 bioRxiv   pre-print
Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image  ...  the structure of the cerebral cortex and its hierarchical organization.  ...  The authors thank Changsong Zhou for helpful discussions and Farid I. Kandil for his suggestions on the denotation of the different types of layers.  ... 
doi:10.1101/427955 fatcat:qodjnpcoyzcwbmcy3mbdq3x4zi

Artificial Musical Intelligence: A Survey [article]

Elad Liebman, Peter Stone
2020 arXiv   pre-print
Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an increasingly prevalent domain of machine learning and artificial  ...  machine learning methods.  ...  Hamel and Eck also explored deep belief nets for both genre classification and automatic tagging, and have shown their learned features to outperform the standard MFCC features [256].  ... 
arXiv:2006.10553v1 fatcat:2j6i27wrsfawpgcr2unxdgngd4

Involvement of Machine Learning Tools in Healthcare Decision Making

Senerath Mudalige Don Alexis Chinthaka Jayatilake, Gamage Upeksha Ganegoda, Massimo Martorelli
2021 Journal of Healthcare Engineering  
In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making.  ...  In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden  ...  SVM classifier is used for the prediction. e features in the final convolutional layer can be directly given to the SVM as an input.  ... 
doi:10.1155/2021/6679512 pmid:33575021 pmcid:PMC7857908 fatcat:tkjpjybmife4vhugy4gq3f2tiy

Multi-view Convolutional Neural Networks for 3D Shape Recognition [article]

Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller
2015 arXiv   pre-print
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel  ...  We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images.  ...  Acknowledgements We thank Yanjie Li for her help on rendering meshes. We thank NVIDIA for their generous donation of GPUs used in this research. Our work was partially supported by NSF (CHS-1422441).  ... 
arXiv:1505.00880v3 fatcat:gvs6gyzkmrdnrcqqpemd4o2xyu

High-level event recognition in unconstrained videos

Yu-Gang Jiang, Subhabrata Bhattacharya, Shih-Fu Chang, Mubarak Shah
2012 International Journal of Multimedia Information Retrieval  
In this paper, we review current technologies for complex event recognition in unconstrained videos.  ...  While the existing solutions vary, we identify common key modules and provide detailed descriptions along with some insights for each of them, including extraction and representation of low-level features  ...  Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.  ... 
doi:10.1007/s13735-012-0024-2 fatcat:mfzttic3svb4tho2xb6aczgp4y

Deep learning in agriculture: A survey

Andreas Kamilaris, Francesc X. Prenafeta-Boldú
2018 Computers and Electronics in Agriculture  
Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential.  ...  As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture.  ...  one of SVM and KNN.  ... 
doi:10.1016/j.compag.2018.02.016 fatcat:6ku7oneaorbm3miekfenus6lxe

Automatic Semantic Characterization Of Drum Sounds

António Sá Pinto, Perfecto Herrera
2015 Zenodo  
, traduced in acoustic and psycho-acoustic signal features.  ...  This thesis focuses on the study of the automatic characterization of percussive samples, specifically through the use of semantic descriptors.  ...  One of the big promises of deep learning is the replacement of tediously handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning; this automated learning of  ... 
doi:10.5281/zenodo.1164986 fatcat:px7yat6gine2pbosk2jjrvocpa
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