Journals starting with emot

EmotionComp17 * *Action, Gesture, and Emotion Recognition Competitions: Large Scale Multimodal Gesture Recognition and Real Versus Fake Expressed Emotions
* Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-Temporal Handcrafted Features and Deep Strategies
* Combining Sequential Geometry and Texture Features for Distinguishing Genuine and Deceptive Emotions
* Continuous Gesture Recognition with Hand-Oriented Spatiotemporal Feature
* Darwintrees for Action Recognition
* Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks
* Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations
* Gesture and Sign Language Recognition with Temporal Residual Networks
* Large-Scale Multimodal Gesture Recognition Using Heterogeneous Networks
* Large-Scale Multimodal Gesture Segmentation and Recognition Based on Convolutional Neural Networks
* Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
* Learning Spatiotemporal Features Using 3DCNN and Convolutional LSTM for Gesture Recognition
* Multimodal Gesture Recognition Based on the ResC3D Network
* Particle Filter Based Probabilistic Forced Alignment for Continuous Gesture Recognition
* Real vs. Fake Emotion Challenge: Learning to Rank Authenticity from Facial Activity Descriptors
* Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction
* Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges
* Two-Stream Flow-Guided Convolutional Attention Networks for Action Recognition
* Visualizing Apparent Personality Analysis with Deep Residual Networks
19 for EmotionComp17

EmotionElicitation06 * *Handbook of Emotion Elicitation and Assessment, The
* Observer-based measurement of facial expression with the Facial Action Coding System
* Use of automated facial image analysis for measurement of emotion expression

EmotioNet20 * *Challenges and Promises to Inferring Emotion From Images and Video
* Challenges in Recognizing Spontaneous and Intentionally Expressed Reactions to Positive and Negative Images
* Discriminant Distribution-Agnostic Loss for Facial Expression Recognition in the Wild
* Facial Action Unit Recognition in the Wild with Multi-Task CNN Self-Training for the EmotioNet Challenge
* Multiple Transfer Learning and Multi-label Balanced Training Strategies for Facial AU Detection In the Wild
* Predicting Sentiments in Image Advertisements using Semantic Relations among Sentiment Labels
* TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning
7 for EmotioNet20

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