facial-expression-recognition

Maintainer: phamquiluan

Total Score

14

Last updated 5/17/2024
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Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

The facial-expression-recognition model is a deep learning-based system for recognizing facial expressions. It was developed by phamquiluan, a researcher at Replicate. This model uses a Residual Masking Network (RMN) architecture, which combines residual connections and attention-based masking to enhance the model's ability to capture subtle facial features. The RMN approach is similar to other deep learning models for facial analysis, such as GFPGAN, CodeFormer, and Real-ESRGAN, which focus on restoring and enhancing facial features in images.

Model inputs and outputs

The facial-expression-recognition model takes an image as input and outputs a prediction of the dominant facial expression in the image. The model can recognize seven basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral.

Inputs

  • Image: A single RGB image containing a human face.

Outputs

  • Emotion prediction: A classification of the dominant facial expression in the input image, from the seven basic emotions.

Capabilities

The facial-expression-recognition model is capable of accurately identifying the emotional state of a person based on their facial features. It can be useful in applications such as human-computer interaction, emotion-aware user interfaces, and sentiment analysis. The model has been benchmarked on the FER2013 dataset and achieved state-of-the-art performance, with an accuracy of 76.82%.

What can I use it for?

The facial-expression-recognition model can be used in a variety of applications that require understanding human emotions and facial expressions. For example, it could be integrated into video conferencing software to provide real-time emotion analysis of participants, or used in market research to gauge customer reactions to products or advertisements. Additionally, the model could be used in mental health applications to monitor patient mood and provide personalized support.

Things to try

One interesting thing to try with the facial-expression-recognition model is to use it in combination with other computer vision models, such as Stable Diffusion or VQFR, to create novel applications that integrate facial expression recognition with image generation or manipulation. For example, you could develop a tool that generates personalized artwork based on a user's emotional state, as detected by the facial expression recognition model.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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