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Kernelized dense layers for facial expression recognition [article]

M.Amine Mahmoudi, Aladine Chetouani, Fatma Boufera, Hedi Tabia
2020 arXiv   pre-print
We apply this method to Facial Expression Recognition (FER) and evaluate its performance on RAF, FER2013 and ExpW datasets.  ...  In this paper, we propose a Kernelized Dense Layer (KDL) which captures higher order feature interactions instead of conventional linear relations.  ...  INTRODUCTION Facial Expression Recognition (FER) research aims at classifying the human emotions given from facial images as one of seven basic emotions: happiness, sadness, fear, disgust, anger, surprise  ... 
arXiv:2009.10814v1 fatcat:k7nvnrtukneynn4m636wg36uli

Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks [article]

Sai Prasanna Teja Reddy, Surya Teja Karri, Shiv Ram Dubey, Snehasis Mukherjee
2019 arXiv   pre-print
This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework.  ...  Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans.  ...  with 32 filters of dimension 3 x 3 x 15, one 3D pooling layer with a kernel size 3 x 3 x 3 and two fully-connected (dense) layers.  ... 
arXiv:1904.01390v1 fatcat:zcbc33xyozcbnekyzkpaytqphi

Facial expression recognition in the wild based on multimodal texture features

Bo Sun, Liandong Li, Guoyan Zhou, Jun He
2016 Journal of Electronic Imaging (JEI)  
For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image  ...  For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture.  ...  For dense SIFT descriptor, the bag of words model has shown remarkable performance on facial expression recognition. 22 First, we extract multiscale dense SIFT descriptors 32 from 100 randomly picked  ... 
doi:10.1117/1.jei.25.6.061407 fatcat:gtn6qycghzhjhnnd2uoauopuei

Convolutional Neural Network Based Facial Expression Recognition Using Image Filtering Techniques

Ritanshi Agarwal, Meerut Institute of Engineering and Technology, Neha Mittal, Hanmandlu Madasu, Meerut Institute of Engineering and Technology, Maturi Venkata Subba Rao Engineering College
2021 International Journal of Intelligent Engineering and Systems  
Facial expression recognition poses problems when we attempt to use Convolutional Neural Network (CNN) architectures.  ...  Facial expressions are indicative of one's mood that serves to communicate his/her the status of mind.  ...  Acknowledgments The authors gratefully acknowledge the support of the Vision club of Department of Electronics and Communication Engineering at Meerut Institute of Engineering and Technology, Meerut for  ... 
doi:10.22266/ijies2021.1031.08 fatcat:sdkyjt3bajab7b5bn2invszvpy

Comparing Methods for Assessment of Facial Dynamics in Patients with Major Neurocognitive Disorders [chapter]

Yaohui Wang, Antitza Dantcheva, Jean-Claude Broutart, Philippe Robert, Francois Bremond, Piotr Bilinski
2019 Lecture Notes in Computer Science  
We have adapted these methods from prominent action recognition methods and our promising results suggest that the methods generalize well to the context of facial dynamics.  ...  In this work we compare methods for assessing facial dynamics such as talking, singing, neutral and smiling in AD-patients, captured during music mnemotherapy sessions.  ...  We note that we tested existing methods in expression recognition, such as smile detectors 8 , [4] on the ADP -dataset, as well as facial-landmark based expression recognition algorithms without success  ... 
doi:10.1007/978-3-030-11024-6_10 fatcat:swcl43rirrhvxog6y2a7egk66e

Generating Dataset For Large-scale 3D Facial Emotion Recognition [article]

Faizan Farooq Khan, Syed Zulqarnain Gilani
2021 arXiv   pre-print
The tremendous development in deep learning has led facial expression recognition (FER) to receive much attention in the past few years.  ...  We also develop a deep convolutional neural network(CNN) for 3D FER trained on 624,000 3D facial scans. The test data comprises 208,000 3D facial scans.  ...  Deep Learning based 3D Facial Expression Recognition Deep CNN's for FER of six basic expressions is proposed in (17) .  ... 
arXiv:2109.08043v1 fatcat:inbcil7zrvctdjtvq4b43iltqi

Facial Emotions Recognition using Convolutional Neural Net [article]

Faisal Ghaffar
2020 arXiv   pre-print
The dropout rate for dense layer was 20%.  ...  The facial expression recognition is an active research area. In this project, we worked on recognition of seven basic human emotions.  ...  Recognizing an emotion or facial expression recognition from a facial image is an interesting and challenging problem in computer vision field.  ... 
arXiv:2001.01456v1 fatcat:7toasglnefga5dxzjitjehttmu

Separable convolutional neural networks for facial expressions recognition

Andry Chowanda
2021 Journal of Big Data  
Automatic facial expressions recognition is one technique to automatically capture, recognise, and understand emotions from the interlocutor.  ...  Many techniques proposed to increase the accuracy of emotions recognition from facial cues. Architecture such as convolutional neural networks demonstrates promising results for emotions recognition.  ...  Consent for publication Not applicable.  ... 
doi:10.1186/s40537-021-00522-x fatcat:7m45o3nvkne67dlbe7xt3vsj4q

Expression Empowered ResiDen Network for Facial Action Unit Detection [article]

Shreyank Jyoti, Abhinav Dhall
2018 arXiv   pre-print
(2) how useful is the information from a network trained for categorical Facial Expression Recognition (FER) for the task of FAU detection?  ...  The experiments show the usefulness of facial expression information for AU detection. The proposed network achieves state-of-art results on the two databases.  ...  We train a basic CNN model with 4 convolution layers and 3 dense layers and compared the performance of expression recognition with the ResiDen model on RAF-DB dataset.  ... 
arXiv:1806.04957v1 fatcat:3tyceesmcbbwbi3mt5souufzxe

A Facial Expression Recognition Method Using Improved Capsule Network Model

Yifeng Zhao, Deyun Chen, Wenzheng Bao
2020 Scientific Programming  
an improved capsule structure model suitable for expression recognition.  ...  Aiming at the problem of facial expression recognition under unconstrained conditions, a facial expression recognition method based on an improved capsule network model is proposed.  ...  Related Works For the facial expression recognition under nonconstrained condition, scholars have proposed many methods.  ... 
doi:10.1155/2020/8845176 fatcat:pyaxttz2pfd25myh7mxw74cbkm

IMPROVING CNN FEATURES FOR FACIAL EXPRESSION RECOGNITION

Ahmet Serdar Karadeniz, Mehmet Fatih Karadeniz, Gerhard Wilhelm Weber, Ismail Husein
2019 ZERO Jurnal Sains Matematika dan Terapan  
<span class="fontstyle0">Abstract </span><span class="fontstyle2">Facial expression recognition is one of the challenging tasks in computer<br />vision.  ...  <br /></span><span class="fontstyle0">Key Word</span><span class="fontstyle3">: </span><span class="fontstyle2">Neural network, facial expression recognition, handcrafted features</span> <br /><br />  ...  INTRODUCTION Facial expression recognition (FER) is a system for inferring the emotions of people from images.  ... 
doi:10.30829/zero.v3i1.5881 fatcat:t2oso27kababhejnt2beqthk6u

Predicting Video features from EEG and Vice versa [article]

Gautam Krishna, Co Tran, Mason Carnahan, Ahmed Tewfik
2020 arXiv   pre-print
Our results demonstrate the first step towards synthesizing high quality facial or lip video from recorded EEG features. We demonstrate results for a data set consisting of seven subjects.  ...  In this paper we explore predicting facial or lip video features from electroencephalography (EEG) features and predicting EEG features from recorded facial or lip video frames using deep learning models  ...  The first time distributed dense layer contained 10000 hidden units and the final time distributed dense layer contained 100 hidden units.  ... 
arXiv:2005.11235v1 fatcat:ftik2qvk3vdzjdcwtwaw7rjxwy

Lightweight CNN-based Expression Recognition on Humanoid Robot

2020 KSII Transactions on Internet and Information Systems  
Therefore, this paper proposed an expression recognition framework based on densely concatenated convolutional neural networks to balance accuracy and latency and apply it to humanoid robots.  ...  The human expression contains a lot of information that can be used to detect complex conditions such as pain and fatigue.  ...  The test device used a screen-integrated 2-megapixel camera that is sufficient for facial expression recognition in images.  ... 
doi:10.3837/tiis.2020.03.015 fatcat:i43sh6u46beznn3apzgmape35e

Facial expression recognition via ResNet-50

Bin Li, Dimas Lima
2021 International Journal of Cognitive Computing in Engineering  
Human emotion recognition based on facial expressions is of great significance in the application of intelligent human-computer interaction.  ...  In this content, we propose a method of feature extraction using the deep residual network ResNet-50, which combines convolutional neural network for facial emotion recognition.  ...  The first thing to do for facial expression recognition is to preprocess the collected images, then carry out feature extraction and classification recognition.  ... 
doi:10.1016/j.ijcce.2021.02.002 fatcat:q7hl6pc7qzcpdosrpehfwiqz2q

Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition [article]

Aravind Ravi
2018 arXiv   pre-print
The results show that representations learned from pre-trained networks for a task such as object recognition can be transferred, and used for facial expression recognition.  ...  Facial expression recognition has been an active area in computer vision with application areas including animation, social robots, personalized banking, etc.  ...  A method for facial expression recognition based on transfer learning techniques is studied in this work.  ... 
arXiv:1812.06387v1 fatcat:s2jxenpfozdn3f7hpwyow4qaua
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