Facial expression detector

Last update: Apr 20, 2022

Overview

Facial expression detector

This is a multiclass clasification model that detects the facial expressions of a given picture. To detect and crop the faces, it uses MTCNN. Then, with a tensorflow CNN, this algorithm classifies individual faces into one of the following classes: 'angry', 'disgusted', 'fearful', 'happy', 'neutral', 'sad', 'surprised'

The MTCNN model is pretrained, and we use it without fine-tunning to this task, in order to maximize generalization.

The CNN tensorflow model is trained using EarlyStopping, in order to stop training when the val_categorical_accuracy has stopped improving.

Owner

Carlos Tardón Rubio

Computer Scientist and Mathematician.

GitHub Repository

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Facial expression detector

Related tags

Overview

Facial expression detector

Owner

Carlos Tardón Rubio

Low-code/No-code approach for deep learning inference on devices

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