723 Hits in 2.6 sec

Dynamic Emotion Modeling with Learnable Graphs and Graph Inception Network [article]

A. Shirian, S. Tripathi, T. Guha
2021 arXiv   pre-print
emotional cues: facial expressions, speech and body gestures.  ...  Human emotion is expressed, perceived and captured using a variety of dynamic data modalities, such as speech (verbal), videos (facial expressions) and motion sensors (body gestures).  ...  Emotion recognition. Facial emotion recognition.: Recognizing facial expressions is the most common way of analyzing emotion.  ... 
arXiv:2008.02661v2 fatcat:xkxbvyve3fbylpve374zstvrem

Disentangling 3D/4D Facial Affect Recognition with Faster Multi-view Transformer

Muzammil Behzad, Xiaobai Li, Guoying Zhao
2021 IEEE Signal Processing Letters  
In this paper, we propose MiT: a novel multi-view transformer model 1 for 3D/4D facial affect recognition.  ...  Additionally, we offer multi-view weights that are trainable and learnable, and help substantially in training.  ...  We add learnable classification token to the sequence for each view, and for classification, we add extra learnable multi-view weights.  ... 
doi:10.1109/lsp.2021.3111576 fatcat:m342bcxm3fgg7otia6atrr5vne


Maulin Patel, Manisha Patel
2021 International Journal of Engineering Applied Sciences and Technology  
on Facial Emotion Recognition.  ...  Classification of human emotion is done by using a different combination of convolutional neural networks (CNN) that task is known as Facial Emotion Recognition (FER).  ...  Facial expression recognition can play a bigger role in that. For machines, recognition of a facial expression is a wellknown task in the branch that studies computer vision.  ... 
doi:10.33564/ijeast.2021.v05i11.026 fatcat:pcnuffwyxzbmtawasyxh4jooeq

EXPERTNet Exigent Features Preservative Network for Facial Expression Recognition [article]

Monu Verma, Jaspreet Kaur Bhui, Santosh Vipparthi, Girdhari Singh
2019 arXiv   pre-print
Thus, automatic facial expression recognition is an interesting and crucial task to interpret the humans cognitive state through the machine.  ...  ExFeat block preserves the spatial structural information of the facial expression, which allows to discriminate between different classes of facial expressions.  ...  The author would like to thank our Vision Intelligence lab group for their valuable support. We are also thankful to NVIDIA for providing TITAN XP GPU grant.  ... 
arXiv:1904.06658v1 fatcat:23u2vua2fjfv3ipq3fhecnpv6e

A Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition System [article]

Sze-Teng Liong and Y.S. Gan and John See and Huai-Qian Khor
2019 arXiv   pre-print
In the recent year, the state-of-the-arts of facial micro-expression recognition task have been significantly advanced by the emergence of data-driven approaches based on deep learning.  ...  In this paper, we aim to design a shallow network to extract the high level features of the micro-expression details.  ...  The recognition accuracy reported is ∼63% in CASME II in leave one-subject-out cross validation (LOSOCV) protocol, but the number of learnable parameters (weights and biases) in the network is very large  ... 
arXiv:1902.03634v1 fatcat:7x3m5csnqbe7rdorobaom2oriy

Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition [article]

Zhaoqiang Xia, Wei Peng, Huai-Qian Khor, Xiaoyi Feng, Guoying Zhao
2020 arXiv   pre-print
Composite-database micro-expression recognition is attracting increasing attention as it is more practical to real-world applications.  ...  degrade their performance, especially for deep models.  ...  micro-expression recognition.  ... 
arXiv:2006.09674v1 fatcat:f6jzhnhlebaltny35wqlz4dlmi

Facial Expression Recognition Using Transfer Learning on Deep Convolutional Network

Ramchand Hablani
2020 Bioscience Biotechnology Research Communications  
A novel method is proposed for facial expression recognition. We have implemented two techniques for automatic facial expression recognition.  ...  First, we applied transfer learning to AlexNet, and VGG19 for classification. Second, we used AlexNet and Vgg19 for feature extraction and cascaded it with an SVM for classification.  ...  For the task of facial expression recognition, that much large data set is not available.  ... 
doi:10.21786/bbrc/13.14/44 fatcat:tkdmapv2u5gmhbgsg3yeoj5tta

Facial Expression Recognition Method Combined with Attention Mechanism

Ming Chen, Junqiang Cheng, Zhifeng Zhang, Yuhua Li, Yi Zhang, Sang-Bing Tsai
2021 Mobile Information Systems  
Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed.  ...  recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.  ...  In different environments, facial expressions have many functions for communication.  ... 
doi:10.1155/2021/5608340 fatcat:cwbvv3ywebaoroianr7grenmka

Deep generative-contrastive networks for facial expression recognition [article]

Youngsung Kim, ByungIn Yoo, Youngjun Kwak, Changkyu Choi, Junmo Kim
2019 arXiv   pre-print
As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult.  ...  In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose.  ...  ACKNOWLEDGMENT The authors would like to thank members of Face Intelligence project for their kind helps and the supercomputer center for their GPU-server supports at SAIT.  ... 
arXiv:1703.07140v3 fatcat:6vngvw26ejezxfqmjizz4e6luq

Facial Expression Recognition Using a Hybrid CNN–SIFT Aggregator [chapter]

Tee Connie, Mundher Al-Shabi, Wooi Ping Cheah, Michael Goh
2017 Lecture Notes in Computer Science  
Deriving an effective facial expression recognition component is important for a successful human-computer interaction system. Nonetheless, recognizing facial expression remains a challenging task.  ...  This paper describes a novel approach towards facial expression recognition task.  ...  Related Work Automatic recognition of facial expressions has been an active research for a long time.  ... 
doi:10.1007/978-3-319-69456-6_12 fatcat:2ztgfabjpfcevbefnqa52c7iiy

Video-based Facial Expression Recognition using Graph Convolutional Networks [article]

Daizong Liu, Hongting Zhang, Pan Zhou
2020 arXiv   pre-print
Facial expression recognition (FER), aiming to classify the expression present in the facial image or video, has attracted a lot of research interests in the field of artificial intelligence and multimedia  ...  In addition, a weight assignment mechanism is also designed to weight the output of different nodes for final classification by characterizing the expression intensities in each frame.  ...  To sum up, we propose a novel GCN based end-to-end framework for dynamic FER task, called Facial Expression Recognition GCN (FER-GCN), to learn more contributing facial expression features to capture dynamic  ... 
arXiv:2010.13386v1 fatcat:tb46yiaep5hahiq4uiwesrurkq

MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition

Weiwei Cai, Ming Gao, Runmin Liu, Jie Mao
2021 Frontiers in Psychology  
Understanding human emotions and psychology is a critical step toward realizing artificial intelligence, and correct recognition of facial expressions is essential for judging emotions.  ...  However, the differences caused by changes in facial expression are very subtle, and different expression features are less distinguishable, making it difficult for computers to recognize human facial  ...  Therefore, they proposed a weighted projection LBP feature extraction algorithm for different information regions, and improved the accuracy of expression recognition by cascading the weighted features  ... 
doi:10.3389/fpsyg.2021.762795 pmid:34744943 pmcid:PMC8569934 fatcat:cab7sqna3zesppwzfcptijsiz4

Facial Affect "In-the-Wild": A Survey and a New Database

Stefanos Zafeiriou, Athanasios Papaioannou, Irene Kotsia, Mihalis Nicolaou, Guoying Zhao
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Facial Affect "in-thewild": A survey and a new database. Abstract Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis.  ...  Finally, we attempt a significant step further by proposing a novel, comprehensive benchmark that can be utilized for evaluating and training various methodologies for the problems of facial affect, behaviour  ...  Deep Learning methodologies for facial expression recognition "in-the-wild" In the recent EmotiW series of competitions, many methodologies have been applied based on hand-crafted and learnable features  ... 
doi:10.1109/cvprw.2016.186 dblp:conf/cvpr/ZafeiriouPKNZ16 fatcat:vabfqasierfv3euaw42wr6uiki

Robust Facial Expression Recognition with Convolutional Visual Transformers [article]

Fuyan Ma, Bin Sun, Shutao Li
2021 arXiv   pre-print
Facial Expression Recognition (FER) in the wild is extremely challenging due to occlusions, variant head poses, face deformation and motion blur under unconstrained conditions.  ...  Different from previous pure CNNs based methods, we argue that it is feasible and practical to translate facial images into sequences of visual words and perform expression recognition from a global perspective  ...  ., [22] proposed to recover facial feature points for the recognition of facial expressions in the presence of occlusion.  ... 
arXiv:2103.16854v2 fatcat:q4nqmbvehfagzkvrb7p424yd6q

Emotion Detection using Facial Recognition Technique

Ritvik Tiwari, Rudra Thorat, Vatsal Abhani, Shakti Mahapatro
2021 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
Emotion recognition based on facial expression is an intriguing research field, which has been presented and applied in various spheres such as safety, health and in human machine interfaces.  ...  Researchers in this field are keen in developing techniques that can prove to be an aid to interpret, decode facial expressions and then extract these features in order to achieve a better prediction by  ...  CNN consists of neurons with learnable weights and biases.  ... 
doi:10.32628/cseit2174104 fatcat:3mgtv3kgejhgfc3p4i3m6gy2fq
« Previous Showing results 1 — 15 out of 723 results