Phamquiluan

facial-expression-recognition

The facial-expression-recognition model is a system that uses a Residual Masking Network to recognize and classify facial expressions in images. It is trained on a dataset of facial expressions labeled with corresponding emotions. The model takes an input image of a face and processes it to identify the facial features and expressions present. It then classifies the expression into one of several predefined categories, such as happiness, sadness, anger, etc. The model uses residual connections and masking techniques to improve accuracy and extract important information from the input images. This enables the model to accurately recognize and classify facial expressions in real-time applications.

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