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Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition

Xue CHEN, Chunheng WANG, Baihua XIAO, Song GAO
2014 IEICE transactions on information and systems  
This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition.  ...  of cross-view data.  ...  In this paper, we propose a novel feature learning method to obtain high-level, domain-robust representations for cross-view face recognition. It combines ideas from both of the previous approaches.  ... 
doi:10.1587/transinf.2014edl8095 fatcat:pzy4bbpfc5djppxu24wvb3fumm

Towards Pose Invariant Face Recognition in the Wild

Jian Zhao, Yu Cheng, Yan Xu, Lin Xiong, Jianshu Li, Fang Zhao, Karlekar Jayashree, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Third, DLN is a generic Convolutional Neural Network (CNN) for face recognition with our enforced cross-entropy optimization strategy for learning discriminative yet generalized feature representation.  ...  training and a "learning to learn" strategy for high-fidelity and identity-preserving frontal view synthesis.  ...  very large poses and learn pose invariant representations for face recognition in the wild.The intermediate results of recovered face images in the frontal view and learned facial representations are  ... 
doi:10.1109/cvpr.2018.00235 dblp:conf/cvpr/ZhaoCXXL0KPSXYF18 fatcat:5puya4rcgfgernddrontafdz5q

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., Ensemble of Deep Convolutional Neural Networks With Gabor Face Representations for Face Recognition.  ...  ., +, TIP 2020 1972-1984 Cross-View Gait Recognition by Discriminative Feature Learning.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIP 2021 7127-7142 Multi-View Face Synthesis via Progressive Face Flow. Xu, Y., +, TIP 2021 6024-6035 Multi-Scale Representation Learning on Hypergraph for 3D Shape Retrieval and Recognition.  ...  ., +, TIP 2021 4371-4383 2021 2734-2744 DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Multi-View Gait Image Generation for Cross-View Gait Recognition. Chen, Based Localization.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition [article]

Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Haochong Lan, Fang Zhao, Lin Xiong, Yan Xu, Jianshu Li, Sugiri Pranata, Shengmei Shen, Junliang Xing, Hengzhu Liu (+2 others)
2018 arXiv   pre-print
Third, we develop effective and novel training strategies for end-to-end learning the whole deep architecture, which generates powerful age-invariant face representations explicitly disentangled from the  ...  As opposed to current techniques for age-invariant face recognition, which either directly extract age-invariant features for recognition, or first synthesize a face that matches target age before feature  ...  2) where L cad is the cross-age domain adversarial loss for facilitating age-invariant representation learning via domain adaption, L cer is the cross-entropy regularization loss for constraining cross-age  ... 
arXiv:1809.00338v2 fatcat:qxg7d64n35fjzay2bdgcajlw4q

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Lai, Q., +, TMM 2021 2086-2099 VehicleNet: Learning Robust Visual Representation for Vehicle Re-Identifi-cation.  ...  ., +, TMM 2021 1640-1653 Adversarial Disentanglement Spectrum Variations and Cross-Modality Attention Networks for NIR-VIS Face Recognition.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

2021 16TH IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

2021 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)  
Self-Supervised Video Pose Representation Learning for Occlusion-Robust Action Recognition Di Yang; Yaohui Wang; Antitza Dantcheva; Lorenzo Garattoni; Gianpiero Francesca; Francois Bremond 5.  ...  Heterogeneous Face Frontalization via Domain Agnostic Learning Xing Di; Shuowen Hu; Vishal Patel 42.  ... 
doi:10.1109/fg52635.2021.9667043 fatcat:q67llypbybbrbacdiyia6hs2pe

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
Wen, J., +, 75-88 Age Factor Removal Network Based on Transfer Learning and Adversarial Learning for Cross-Age Face Recognition.  ...  ., +, TCSVT Nov. 2020 4284-4298 Age Factor Removal Network Based on Transfer Learning and Adversarial Learning for Cross-Age Face Recognition.  ...  A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

Deep Learning Architectures for Face Recognition in Video Surveillance [article]

Saman Bashbaghi, Eric Granger, Robert Sabourin, Mostafa Parchami
2018 arXiv   pre-print
Face recognition (FR) systems for video surveillance (VS) applications attempt to accurately detect the presence of target individuals over a distributed network of cameras.  ...  (e.g., canonical face representation CNN) are reviewed and compared in terms of accuracy and computational complexity.  ...  Moreover, the number of net- Conclusion and Future Directions In this chapter, the most recent deep learning architectures proposed for robust face recognition in video surveillance were thoroughly  ... 
arXiv:1802.09990v2 fatcat:uhur2ecppbhxzafhamqbwqrdg4

Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition

Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Fang Zhao, Jianshu Li, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Third, we develop effective and novel training strategies for end-to-end learning the whole deep architecture, which generates powerful age-invariant face representations explicitly disentangled from the  ...  As opposed to current techniques for ageinvariant face recognition, which either directly extract ageinvariant features for recognition, or first synthesize a face that matches target age before feature  ...  AIM can learn age-invariant representations and synthesize photorealistic cross-age faces effectively. Best viewed in color.  ... 
doi:10.1609/aaai.v33i01.33019251 fatcat:eaf5rdgns5edpnzfwzm7c72yde

Siamese neural network based gait recognition for human identification

Cheng Zhang, Wu Liu, Huadong Ma, Huiyuan Fu
2016 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
In this paper, we have developed a Siamese neural network based gait recognition framework to automatically extract robust and discriminative gait features for human identification.  ...  Different from conventional deep neural network, the Siamese network can employ distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for  ...  Therefore, we verify the robustness of our proposed methods in cross-view gait recognition task.  ... 
doi:10.1109/icassp.2016.7472194 dblp:conf/icassp/ZhangLMF16 fatcat:i4lwl6jlefgynhofz77mmwnodu

NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing [article]

Zitong Yu, Jun Wan, Yunxiao Qin, Xiaobai Li, Stan Z. Li, Guoying Zhao
2020 IEEE Transactions on Software Engineering   pre-print
Besides, we propose Domain/Type-aware Meta-NAS, which leverages cross-domain/type knowledge for robust searching.  ...  Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Existing methods heavily rely on the expert-designed networks, which may lead to a sub-optimal solution for FAS task.  ...  Adversarial learning [55] , fine-grained meta learning [56] and multi-domain disentangled learning [57] are utilized to learn robust features for domain generalization in FAS.  ... 
doi:10.1109/tpami.2020.3036338 pmid:33166249 arXiv:2011.02062v1 fatcat:cshgog3xevagdifydveyfgm7fi

Table of contents

2021 IEEE transactions on circuits and systems for video technology (Print)  
Hu 148 Learning Representations From Skeletal Self-Similarities for Cross-View Action Recognition ........................... ...........................................................................  ...  Image/Video Analysis and Computer Vision A Parallel Architecture of Age Adversarial Convolutional Neural Network for Cross-Age Face Recognition .......... ..............................................  ... 
doi:10.1109/tcsvt.2020.3039667 fatcat:hthz6y3xufak7mc24urgth6poe

Guest Editorial Introduction to the Special Section on Intelligent Visual Content Analysis and Understanding

Hongliang Li, Lu Fang, Tianzhu Zhang
2020 IEEE transactions on circuits and systems for video technology (Print)  
And the designed VI-HC and VID can help learn more robust representations under such situation.  ...  To explore the complementary properties between the hand-crafted shallow feature representation and deep features, "Discriminative multi-view subspace feature learning for action recognition" by Sheng  ... 
doi:10.1109/tcsvt.2020.3031416 fatcat:gpwbmydqbza5lddatxcfcidwcq

CP-GAN: A Cross-Pose Profile Face Frontalization Boosting Pose-Invariant Face Recognition

Jinjin Liu, Qingbao Li, Ming Liu, Tongxin Wei
2020 IEEE Access  
faces from the profiles or abstract pose-robust representations for face recognition in the wild.  ...  In PIM, a complex discriminative learning sub-net is adopted for face verification and recognition.  ... 
doi:10.1109/access.2020.3033675 fatcat:enjgquxsfng7pmrpyheosh4pby
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