CNNs Model To classify Facial Emotions

This project is based on Convolutional Neural Networks (CNNs) to build a model capable of classifying human emotions from facial expressions.

The main goal is to train the model on facial images so it can distinguish between multiple emotion categories such as happiness, sadness, anger, surprise, fear, disgust, and neutrality.

The process includes:

Image preprocessing: resizing, normalization, and converting images into a consistent format (grayscale or RGB).

Building the CNN model: using convolution and pooling layers to extract important facial features.

Training: feeding the model with a large labeled dataset of facial images.

Evaluation: testing the model on unseen data to measure its accuracy and performance.

Applications of this model include:

Human-Computer Interaction (HCI).

Psychological support and emotion monitoring.

Behavioral analysis in marketing or education.

Enhancing user experience in AI-driven applications.