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Rotating your face using multi-task deep neural network

Junho Yim, Heechul Jung, ByungIn Yoo, Changkyu Choi, Dusik Park, Junmo Kim
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
This novel type of multi-task model significantly improves identity preservation over the single task model.  ...  This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination  ...  We train a deep neural network (DNN) that takes a face image and a binary code encoding a target pose, which we call Remote Code, and generates a face image with the same identity viewed at the target  ... 
doi:10.1109/cvpr.2015.7298667 dblp:conf/cvpr/YimJYCPK15 fatcat:d4i5vihpe5hdrbkz345g4sw3py

A Prediction of Emotions for Recognition of Facial Expressions using Deep Learning

2019 International journal of recent technology and engineering  
In this research, we enhanced Convolutional Neural Network method to recognize 6 basic emotions and compared some pre processing methods to show the influences of its in CNN performance.  ...  Many deep learning approaches have been applied in recent years due to their outstanding recognition accuracy after training with large amounts of data.  ...  Decission Tree (DT), Multi-layer perception (MLP),Convolution Neural Network (CNN).The convolution neural network is determined to produce the best recognition accuracy.  ... 
doi:10.35940/ijrte.b1183.0982s1119 fatcat:q2ib6q57ynagpgkqygirs4gwgi

Multi-Label Networks for Face Attributes Classification

Sara Atito Aly, Berrin Yanikoglu
2018 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)  
We have evaluated the idea of using the location knowledge for a particular attribute group to speed up the network training.  ...  In this work, we propose to train attributes in groups based on their localization (head, eyes, nose, cheek, mouth, shoulder, and general areas) in a multi-task learning scenario to speed up the training  ...  CONCLUSION We presented a multi-task framework for face attribute classification based on feature locality.  ... 
doi:10.1109/icmew.2018.8551518 dblp:conf/icmcs/AlyY18 fatcat:67vxj3m2vrforb5tamu55fub3e

The Previous Academic Research Experiences & Works and the Summary of Dissertation for HKU.(Siming Zheng) [article]

Siming Zheng
2020 figshare.com  
The Summary of DissertationThank you for your attention.Applicant name: Siming ZhengWritten date: Nov 20th, 2020.Best regards.  ...  Regarding this Supporting Document:This supporting document includes previous research topics and research presentations in form PPT for your reference.  ...  . 3D FACE RECOGNITION USING HOG FEATURES The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best  ... 
doi:10.6084/m9.figshare.13302275.v1 fatcat:onkj37p6cffqrbq7gqgq7bolxq

The Previous Academic Research Experiences & Works and the Summary of Dissertation (Siming Zheng_ca) [article]

Siming Zheng
2020 figshare.com  
The Summary of DissertationThank you for your attention.Applicant name: Siming ZhengWritten date: Nov 20th, 2020.Best regards.  ...  Regarding this Supporting Document:This supporting document includes previous research topics and research presentations in form PPT for your reference.  ...  . 3D FACE RECOGNITION USING HOG FEATURES The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best  ... 
doi:10.6084/m9.figshare.13298975.v1 fatcat:wwxfpb2drfc3nc47isiulutaq4

The previous Academic Research Experiences & Works and the Summary of Dissertation (Siming Zheng) [article]

Siming Zheng
2020 figshare.com  
The Summary of DissertationThank you for your attention.Applicant name: Siming ZhengWritten date: Nov 20th, 2020.Best regards.  ...  Regarding this Supporting Document:This supporting document includes previous research topics and research presentations in form PPT for your reference.  ...  . 3D FACE RECOGNITION USING HOG FEATURES The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best  ... 
doi:10.6084/m9.figshare.13271060.v1 fatcat:rv2cy2ddczh55ngt3suuxavd6u

The Previous Academic Research Experiences & Works and the Summary of Dissertation (Siming Zheng) [article]

Siming Zheng
2020 figshare.com  
The Summary of DissertationThank you for your attention.Applicant name: Siming ZhengWritten date: Nov 20th, 2020.Best regards.  ...  Regarding this Supporting Document:This supporting document includes previous research topics and research presentations in form PPT for your reference.  ...  . 3D FACE RECOGNITION USING HOG FEATURES The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best  ... 
doi:10.6084/m9.figshare.13271060.v2 fatcat:cn2kocuzmvbfnijrdodh22xqy4

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark [article]

Yihua Cheng, Haofei Wang, Yiwei Bao, Feng Lu
2021 arXiv   pre-print
We summarize the processing pipeline and discuss these methods from four perspectives: deep feature extraction, deep neural network architecture design, personal calibration as well as device and platform  ...  However, it lacks a guideline for designing deep learning algorithms for gaze estimation tasks.  ...  In order to compensate for the low-resolution captured images, they use multi-cameras to capture multi-view images and use a neural network to regress gaze from these images.  ... 
arXiv:2104.12668v1 fatcat:w5wj7be33jhx7oezuepptph4i4

Age Invariant Face Recognition

Prathama V, Thippeswamy G
2019 International Journal of Trend in Scientific Research and Development  
Facial features are extracted using a convolutional neural network characteristic of the deep learning.  ...  Building the CNN Convolutional Neural Network is a class of deep neural network that is used for Computer Vision or analyzing visual imagery. This is most important step for our network.  ...  Activate Tensorflow env and install keras using 'pip install keras'. 5. CNN -Convolution Neural network, a class of deep, feed-forward artificial neural networks.  ... 
doi:10.31142/ijtsrd23572 fatcat:y6turriy5bc7xaml6r4l2lgqyy

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)  
As entering the deep learning era, various powerful deep neural networks further enable an intelligent vision system to cope with more complex scenarios.  ...  In detail, a pretrained VGG-16 convolutional neural network is used to obtain VGG-face descriptions for each subaperture (SA) image corresponding to each observation angle.  ... 
doi:10.1109/tcsvt.2020.3031416 fatcat:gpwbmydqbza5lddatxcfcidwcq

Linear fusion approach to convolutional neural networks for facial emotion recognition

Usen Dudekula, Purnachand N.
2022 Indonesian Journal of Electrical Engineering and Computer Science  
Using a deep network with one channel, the proposed algorithm can achieve well comparable performance.  ...  Final recognition is calculated using convolution neural network architecture followed by a softmax classifier.  ...  Two-stage multi-task framework to explore FER.  ... 
doi:10.11591/ijeecs.v25.i3.pp1489-1500 fatcat:p4g2imoqejewhptnxp3teit4ui

Novel Methods Based on Deep Learning Applied to Condition Monitoring in Smart Manufacturing Processes [chapter]

Francisco Arellano Espitia, Lucia Ruiz Soto
2020 New Trends in the Use of Artificial Intelligence for the Industry 4.0  
Condition-based monitoring (CBM) schemes are the most prominent tool to cover the task of predictive diagnosis.  ...  With the current demand of the industry and the increasing complexity of the systems, it is vital to incorporate CBM methodologies that are capable of facing the variability and complexity of manufacturing  ...  DL is a branch of machine learning based on multi-layer neural networks or deep neural networks (DNNs), where the objective of each layer or level is to learn to transform your input data into a non-linear  ... 
doi:10.5772/intechopen.89570 fatcat:n7f3yfs43fgz3pfmeuyxxalkkq

Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis [article]

Xiaotian Li, Zhihua Li, Huiyuan Yang, Geran Zhao, Lijun Yin
2022 arXiv   pre-print
the "Self-Diversified Multi-Channel Attention Network (SMA-Net)".  ...  A broad range of previous research has explored how to use attention modules to localize detailed facial parts (e,g. facial action units), learn discriminative features, and learn inter-class correlation  ...  evaluated on six widely used benchmark datasets for both AU detection and facial expression recognition. It achieves superior performance over the peer state-of-the-art methods. VI.  ... 
arXiv:2203.12570v1 fatcat:nkp2ivciwfeylfkefoga56orza

A Chronological Review on Face Detection Algorithms Used in Modern Surveillance Systems

Sapna Rathore
2021 International Journal for Research in Applied Science and Engineering Technology  
In this article we have reviewed various prominent approaches for facial detection ranging from classical edge based detection to neural network based model.  ...  Index Terms: Face detection, challenges, noise, features.  ...  The model deploys a Multiple Task Cascaded Neural Network (MTCNN) model of deep learning to identify the human face present in test images.  ... 
doi:10.22214/ijraset.2021.38900 fatcat:toprie6qefe3zovjc6agakgifa

VR content creation and exploration with deep learning: A survey

Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang
2020 Computational Visual Media  
This article surveys recent research that uses such deep learning methods for VR content creation and exploration.  ...  VR content creation and exploration relates to image and video analysis, synthesis and editing, so deep learning methods such as fully convolutional networks and general adversarial networks are widely  ...  Typically, CNN-based deep neural networks are widely used for modelling a face from monocular input. Tran et al.  ... 
doi:10.1007/s41095-020-0162-z fatcat:lgogzx26bvhn5f7uyefjkz7zny
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