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Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM
2014
Journal of Electrical and Computer Engineering
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In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR), which is called Multiview Smooth Discriminant Analysis (MSDA) based on Extreme Learning Machines (ELM). ...
Adding the Laplacian penalty constrain for the multiview feature learning, the proposed MSDA is first proposed to extract the cross-modality 2D-3D face features. ...
Conclusion In this paper, a Multiview Smooth Discriminant Analysis based on ELM method is proposed for cross-modality 2D-3D face recognition. 2D-3D face recognition is an alternative and feasible approach ...
doi:10.1155/2014/584241
fatcat:gqjvpddltnda7ngryopqfyuwti
A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
2020
IEEE Access
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Based on the categorization of the different 3D data representations proposed in this paper, the importance of choosing a suitable 3D data representation which depends on simplicity, usability, and efficiency ...
However, implementing the methods in 3D data is a bit complex because most of the previously designed deep learning architectures used 1D or 2D as input. ...
In this survey, the methods are categorized into four groups based on the modality adopted for recognition which are; RGB-based, depth-based, skeleton-based and RGB+D-based. ...
doi:10.1109/access.2020.2982196
fatcat:jnya5rscynf3zm7efuucqxafri
RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey
2019
IEEE Access
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Object recognition in real-world environments is one of the fundamental and key tasks in computer vision and robotics communities. ...
RGB-D-based object recognition has evolved from early methods that using hand-crafted representations to the current state-of-the-art deep learning-based methods. ...
[87] calculated the features of compactness, symmetry, global convexity, uniqueness, smoothness from the contour-based images (2D) and point cloud data (3D), respectively. ...
doi:10.1109/access.2019.2907071
fatcat:shamfnufhfavjlcnrcldpgqtgq
Challenges in Multi-modal Gesture Recognition
[chapter]
2017
Gesture Recognition
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We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. ...
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. ...
Acknowledgments This work has been partially supported by ChaLearn Challenges in Machine Learning http: //chalearn.org, the Human Pose Recovery and Behavior Analysis Group 7 , the Pascal2 network of excellence ...
doi:10.1007/978-3-319-57021-1_1
fatcat:vfeijghqtvffllogw2tium3pwa
Challenges in multimodal gesture recognition
2016
Journal of machine learning research
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We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. ...
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. ...
Acknowledgments This work has been partially supported by ChaLearn Challenges in Machine Learning http: //chalearn.org, the Human Pose Recovery and Behavior Analysis Group 7 , the Pascal2 network of excellence ...
dblp:journals/jmlr/EscaleraAG16
fatcat:r4q2iywy7balhjlh2vpknltrde
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing
[article]
2020
arXiv
pre-print
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The main applications of ML-based RSP are then analysed and structured based on the application field. ...
Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. ...
The latter seeks to fuse the separate features, learned from multiple different views, into a single compact representation, including multi-modal AEs, multiview CNNs, and multi-modal RNNs. ...
arXiv:2009.13702v1
fatcat:m6am73324zdwba736sn3vmph3i
Automatic Face Understanding: Recognizing Families in Photos
[article]
2021
arXiv
pre-print
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A majority of these networks have objectives based on L1 or L2 norms, which inherit several disadvantages. ...
We also trained CNNs on FIW and deployed the model on the renowned KinWild I and II to gain SOTA. Most recently, we further augmented FIW with MM. ...
This infers that faces encoded via VGG-Face are more discriminative when used off -the-shelf than when metrics are learned on top. ...
arXiv:2102.08941v1
fatcat:eqje3jh23nb6do7crz7rw6342a
Deep Learning in Mobile and Wireless Networking: A Survey
2019
IEEE Communications Surveys and Tutorials
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Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. ...
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. ...
Ji et al. propose 3D convolutional neural networks for video activity recognition [165] , demonstrating superior accuracy as compared to 2D CNN. ...
doi:10.1109/comst.2019.2904897
fatcat:xmmrndjbsfdetpa5ef5e3v4xda
Deep Learning in Mobile and Wireless Networking: A Survey
[article]
2019
arXiv
pre-print
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Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. ...
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. ...
Ji et al. propose 3D convolutional neural networks for video activity recognition [163] , demonstrating superior accuracy as compared to 2D CNN. ...
arXiv:1803.04311v3
fatcat:awuvyviarvbr5kd5ilqndpfsde
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
2021
Information Fusion
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., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring ...
On the other hand, many models in generalized zero-shot learning depend on cross-modal mapping between the class embedding space and the image feature space. Schönfeld et al. ...
for the application of UQ methods. 2D echocardiography is a widely used imaging modality for cardiovascular diseases. ...
doi:10.1016/j.inffus.2021.05.008
fatcat:yschhguyxbfntftj6jv4dgywxm
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
[article]
2021
arXiv
pre-print
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Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field. ...
., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring ...
[95] proposed an uncertainty aware framework based on multi-modal Bayesian fusion for activity recognition. ...
arXiv:2011.06225v4
fatcat:wwnl7duqwbcqbavat225jkns5u