Facial Emotion Recognition
17 papers with code • 2 benchmarks • 6 datasets
Emotion Recognition from facial images
JAFFE
RAVDESS
CANDOR Corpus
Thermal Face Database
MH-FED
HEADSET
Most implemented papers
Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition
One of the most prominent types of demographic bias are statistical imbalances in the representation of demographic groups in the datasets.
An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset
Although real-time facial emotion recognition is a hot topic research domain in the field of human-computer interaction, state-of the-art available datasets still suffer from various problems, such as some unrelated photos such as document photos, unbalanced numbers of photos in each class, and misleading images that can negatively affect correct classification.
HTNet for micro-expression recognition
The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area.
EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
yelboudouri/EmoNeXt
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IEEE 25th International Workshop on Multimedia Signal Processing (MMSP) 2023
Facial expressions play a crucial role in human communication serving as a powerful and impactful means to express a wide range of emotions.
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique
The Resnet50 has demonstrated superior performance, with accuracies of 73. 68% for the person-dependent mode and 59. 57% for the person-independent mode.
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition.
GiMeFive: Towards Interpretable Facial Emotion Classification
werywjw/sep-cvdl
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Deep convolutional neural networks have been shown to successfully recognize facial emotions for the past years in the realm of computer vision.
