Journal ArticleDOI
Variance-guided attention-based twin deep network for cross-spectral periocular recognition
TLDR
Ablation studies and experimental results on three publicly available cross-spectral periocular datasets containing images from VIS, near-infrared (NIR), and night vision domains show that the proposed deep network achieves the state-of-the-art recognition performances on all three datasets.About:
This article is published in Image and Vision Computing.The article was published on 2020-12-01. It has received 15 citations till now. The article focuses on the topics: Periocular Region & Night vision.read more
Citations
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Journal ArticleDOI
Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet
Ying Chen,Huimin Gan,Huiling Chen,Yugang Zeng,Liang-jun Xu,Ali Heidari,Xiaodong Zhu,Yuanning Liu +7 more
TL;DR: Zhang et al. as mentioned in this paper proposed an end-to-end unified framework based on deep learning that does not include normalization in order to achieve improved accuracy in iris segmentation and recognition.
Journal ArticleDOI
Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey
Renu Sharma,Arun Ross +1 more
TL;DR: The performance of face recognition systems can be negatively impacted in the presence of masks and other types of facial coverings that have become prevalent due to the COVID-19 pandemic, so the periocular region of the human face becomes an important biometric cue.
Posted Content
UFPR-Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios.
Luiz A. Zanlorensi,Rayson Laroca,Diego R. Lucio,Lucas R. Santos,Alceu S. Britto,David Menotti +5 more
TL;DR: A new periocular dataset containing samples from 1,122 subjects, acquired in 3 sessions by 196 different mobile devices is presented, and an extensive benchmark with several Convolutional Neural Network architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multitask Learning, Pairwise Filters Network, and Siamese Network is performed.
Journal ArticleDOI
A new periocular dataset collected by mobile devices in unconstrained scenarios
TL;DR: In this paper , the authors presented a new periocular dataset containing samples from 1122 subjects, acquired in 3 sessions by 196 different mobile devices and performed an extensive benchmark with several CNN architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multi-task Learning, Pairwise Filters Network, and Siamese Network.
Posted Content
Periocular in the Wild Embedding Learning with Cross-Modal Consistent Knowledge Distillation.
TL;DR: A deep face-to-periocular distillation networks, coined as cross-modal consistent knowledge distillation (CM-CKD) henceforward, is put forward, which extends identification and verification performance by 50% in terms of relative performance gain computed based upon face and periocular baselines.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI
Rethinking the Inception Architecture for Computer Vision
TL;DR: In this article, the authors explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
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Rethinking the Inception Architecture for Computer Vision
TL;DR: This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.