https://github.com/omidmn/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
Papers with Code 아카이브
Facial Expression Recognition (FER)
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- 평가표
- 25
- 보존된 평가 기록
- 167
Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.
<span style="color:grey; opacity: 0.6">( Image credit: DeXpression )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/omidnezami/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/p0werweirdo/tagfcl
Compacting, Picking and Growing for Unforgetting Continual Learning
https://github.com/phamquiluan/ResidualMaskingNetwork
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/prakharbhardwaj1/emotion-recognition
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/raulsteleac/Speech_Emotion_Recognition
Transfer Learning for Improving Speech Emotion Classification Accuracy
https://github.com/rdgozum/dexpression-pytorch
DeXpression: Deep Convolutional Neural Network for Expression Recognition
https://github.com/rohithv/Grammatical-facial-expression-recognition
Grammatical facial expression recognition using customized deep neural network architecture
https://github.com/shanxuanchen/FacialExpressionRecognition
Island Loss for Learning Discriminative Features in Facial Expression Recognition
https://github.com/shiqiyu/libfacedetection.train
https://github.com/simon20010923/DDAMFN
https://github.com/siqueira-hc/Efficient-Facial-Feature-Learning-with-Wide-Ensemble-based-Convolutional-Neural-Networks
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
https://github.com/sithu31296/EasyFace
Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition
https://github.com/sw-gong/spiralnet_plus
4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications
https://github.com/tarun-98/COMP8240_Project_GroupG
Facial Motion Prior Networks for Facial Expression Recognition
https://github.com/textvec/textvec
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
https://github.com/thanhhungqb/pyramid-super-resolution
https://github.com/tomas-gajarsky/facetorch
Pre-training strategies and datasets for facial representation learning
https://github.com/vicksam/fer-model
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/wonsu0513/ros2_foxy_facial_expressions
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/yijiazh/DFER_Summer2019
https://github.com/zengqunzhao/efficientface
https://github.com/zengqunzhao/ma-net
https://github.com/zveryansky/textvec
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
https://github.com/zyh-uaiaaaa/relative-uncertainty-learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.