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Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training [article]

Shuangping Jin, Zhenhua Feng, Wankou Yang, Josef Kittler
2021 arXiv   pre-print
To address the above issues, this paper presents a novel Separable Batch Normalization (SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust facial landmark localization.  ...  However, existing publicly available datasets used in facial landmark localization are usually much smaller than those for other computer vision tasks.  ...  CONCLUSION In this paper, we presented a novel Separable Batch Normalization (SepBN) module for robust facial landmark localization.  ... 
arXiv:2101.06663v1 fatcat:pnqaqdruc5c3zpclwrtwenmmia

Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network

Daniel Merget, Matthias Rock, Gerhard Rigoll
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Our experiments demonstrate the effectiveness of our approach, outperforming several state-of-the-art methods in facial landmark detection.  ...  The kernel convolution is crucial for the convergence of the network because it smoothens the gradients and reduces overfitting.  ...  Except for the output layers, all layers are subject to batch normalization [14] with ReLU activations and 10% dropout.  ... 
doi:10.1109/cvpr.2018.00088 dblp:conf/cvpr/MergetRR18 fatcat:txttqdcblncxzidh6uu64ywwsq

Attention-Driven Cropping for Very High Resolution Facial Landmark Detection

Prashanth Chandran, Derek Bradley, Markus Gross, Thabo Beeler
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Facial landmark detection is a fundamental task for many consumer and high-end applications and is almost entirely solved by machine learning methods today.  ...  In addition to being the first method for facial landmark detection on high resolution images, our approach achieves superior performance over traditional (holistic) state-of-the-art architectures across  ...  The second stage then localizes the landmarks in this frontalized zoom-in, which further reduces variability and increases robustness and accuracy.  ... 
doi:10.1109/cvpr42600.2020.00590 dblp:conf/cvpr/ChandranBGB20 fatcat:zg2heo4y5re2dnan75leekrw7e

Convolutional and Recurrent Neural Networks for Face Image Analysis

Kıvanç Yüksel, Władysław Skarbek
2019 Foundations of Computing and Decision Sciences  
The Face Parts Detector inputs face image and outputs the pixel coordinates of bounding boxes for detected facial parts.  ...  The Face Alignment Network extracts deep features in CNN module while in the recurrent module it generates 68 facial landmarks using not only this deep features, but also the geometry of facial parts.  ...  Acknowledgment This work was partially co-financed by the National Centre for Research and Development in Poland funds as part of the project POIR.01.01.01-00-0800/17 developed by the Promity, Warsaw,  ... 
doi:10.2478/fcds-2019-0017 fatcat:bs4tzpgvxfa6falgamrzzm45pm

Learning to Validate the Quality of Detected Landmarks [article]

Wolfgang Fuhl, Enkelejda Kasneci
2020 arXiv   pre-print
We conducted experiments on the 300W, AFLW, and WFLW facial landmark datasets.  ...  We present a new loss function for the validation of image landmarks detected via Convolutional Neural Networks (CNN). The network learns to estimate how accurate its landmark estimation is.  ...  We especially thank our partners Benedikt Rombach, Martin Mähler and Hildegard Gerhardy from IBM for their expertise and support.  ... 
arXiv:1901.10143v3 fatcat:ps3yxxkfjzgvbinhul3dwlxdey

Deep Multi-Center Learning for Face Alignment

Zhiwen Shao, Hengliang Zhu, Xin Tan, Yangyang Hao, Lizhuang Ma
2019 Neurocomputing  
Facial landmarks are highly correlated with each other since a certain landmark can be estimated by its neighboring landmarks.  ...  Most of the existing deep learning methods only use one fully-connected layer called shape prediction layer to estimate the locations of facial landmarks.  ...  the expectation and variance of Batch Normalization [29] , and the scaling and shifting of Batch Normalization.  ... 
doi:10.1016/j.neucom.2018.11.108 fatcat:3hf36qjv7bh75lzkx76tgkxbuy

Unsupervised learning from local features for video-based face recognition

Ajmal Mian
2008 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition  
The algorithm is inherently robust to large scale occlusions as it relies on local features.  ...  The proposed algorithm exploits spatiotemporal information obtained from local features that are extracted from arbitrary keypoints on faces as opposed to pre-defined landmarks.  ...  Acknowledgments Thanks to UCSD for providing the data and D. Lowe for providing the SIFT code. This research is sponsored by ARC Discovery grant DP0881813 and partly by UWA Research Grant 2007.  ... 
doi:10.1109/afgr.2008.4813310 dblp:conf/fgr/Mian08 fatcat:d43edl5ix5dnxmyeypytgwmqtq

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training

Jianshu Li, Shengtao Xiao, Fang Zhao, Jian Zhao, Jianan Li, Jiashi Feng, Shuicheng Yan, Terence Sim
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
Specifically, iFAN achieves an overall F-score of 91.15% on the Helen dataset for face parsing, a normalized mean error of 5.81% on the MTFL dataset for facial landmark localization and an accuracy of  ...  It consists of a number of tasks, such as facial emotion recognition and face parsing, and most existing approaches generally treat these tasks independently, which limits their deployment in real scenarios  ...  Facial Landmark Localization. For facial landmark localization, we report the results on two widely used metrics [25, 29] , i.e. normalized mean error and failure rate.  ... 
doi:10.1145/3123266.3123438 dblp:conf/mm/LiXZZLFYS17 fatcat:l6ykbudao5df5ndijg6zk7ihj4

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training [article]

Jianshu Li, Shengtao Xiao, Fang Zhao, Jian Zhao, Jianan Li, Jiashi Feng, Shuicheng Yan, Terence Sim
2017 arXiv   pre-print
Specifically, iFAN achieves an overall F-score of 91.15% on the Helen dataset for face parsing, a normalized mean error of 5.81% on the MTFL dataset for facial landmark localization and an accuracy of  ...  It consists of a number of tasks, such as facial emotion recognition and face parsing, and most existing approaches generally treat these tasks independently, which limits their deployment in real scenarios  ...  Facial Landmark Localization. For facial landmark localization, we report the results on two widely used metrics [25, 29] , i.e. normalized mean error and failure rate.  ... 
arXiv:1711.06055v1 fatcat:f5recx7rwnecjmilttiruptbk4

Multiscale recurrent regression networks for face alignment

Caixun Wang, Haomiao Sun, Jiwen Lu, Jianjiang Feng, Jie Zhou
2017 Applied Informatics  
While local features are utilized to transform and vote for facial landmark detection, the Abstract In this paper, we propose an end-to-end multiscale recurrent regression networks (MSRRN) approach for  ...  Both methods aim at maximizing the joint posterior probability over landmarks for the given facial images.  ...  Availability of data and materials For the experimental evaluation, we leveraged the standard benchmarking dataset, which can be found on the following website: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations  ... 
doi:10.1186/s40535-017-0042-5 fatcat:7hkc3p3l2zhbjl5xz2koekxmse

Multi-Domain Multi-Definition Landmark Localization for Small Datasets [article]

David Ferman, Gaurav Bharaj
2022 arXiv   pre-print
Training a small dataset alongside a large(r) dataset helps with robust learning for the former, and provides a universal mechanism for facial landmark localization for new and/or smaller standard datasets  ...  We present a novel method for multi image domain and multi-landmark definition learning for small dataset facial localization.  ...  facial localization for multiple image domains.  ... 
arXiv:2203.10358v1 fatcat:2gewm6ue2ndlnedoxokytp7rhq

PropagationNet: Propagate Points to Curve to Learn Structure Information [article]

Xiehe Huang, Weihong Deng, Haifeng Shen, Xiubao Zhang, Jieping Ye
2020 arXiv   pre-print
Concretely, we present a novel structure-infused face alignment algorithm based on heatmap regression via propagating landmark heatmaps to boundary heatmaps, which provide structure information for further  ...  Moreover, we propose a Focal Wing Loss for mining and emphasizing the difficult samples under in-the-wild condition.  ...  Experiments Evaluation Metrics Normalized Mean Error (NME) is a widely used metric to evaluate the performance of a facial landmark localization algorithm.  ... 
arXiv:2006.14308v1 fatcat:twuq4u25qra5rlfi636mu3nt5e

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

A F M Saifuddin Saif, Zainal Rasyid
2020 International Journal of Advanced Computer Science and Applications  
In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations  ...  This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially.  ...  ACKNOWLEDGMENT The authors would like to thank Universiti Kebangsaan Malaysia for providing financial support under the "Geran Universiti Penyelidikan" research grant, GUP-2020-064.  ... 
doi:10.14569/ijacsa.2020.0111043 fatcat:fpklth3qrbdfncf5kuesazsynq

Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling [article]

Jiyuan Cao, Zhilei Liu, Yong Zhang
2022 arXiv   pre-print
Facial Action Unit (AU) detection is a crucial task for emotion analysis from facial movements.  ...  The first sub-task is meta-learning-based AU local region representation learning, called MARL, which learns discriminative representation of local AU regions that incorporates the shared information of  ...  Specifically, we capture adaptive AU regions by using facial landmarks. Figure 2 illustrates the correspondence between facial AUs and facial landmarks.  ... 
arXiv:2205.08787v1 fatcat:hqbbuioiojgczgrbxjsbu3sq5y

Facial Component-Landmark Detection With Weakly-Supervised LR-CNN

Ruiheng Zhang, Chengpo Mu, Min Xu, Lixin Xu, Xiaofeng Xu
2019 IEEE Access  
Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks.  ...  In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously.  ...  They build an accurate and robust facial landmark localizer using deep learning tools, which includes two levels of convolutional neural network for course-to-fine prediction.  ... 
doi:10.1109/access.2018.2890573 fatcat:czjdhb4xujeq7hf3q6wzwdnqiu
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