Facial expression recognition
Learning visual expression patterns with a CNN.
Project overview
A convolutional neural network trained on FER-2013 to classify facial expressions. The reported validation accuracy reached 65.4% after 75 epochs.
The engineering
The challenge
Classify facial expressions from images in the FER-2013 dataset using a convolutional neural network.
My contribution
Prepared image inputs, trained the CNN and evaluated its validation accuracy.
Approach & implementation
Applied image preprocessing, supervised CNN training and validation. Trained the model for 75 epochs using Python and PyTorch.
Results & lessons
What the project achieved
Reached a reported validation accuracy of 65.4% after 75 epochs.
Limitations & next steps
Validation accuracy describes performance on the validation split. Dataset expression labels do not establish a person’s actual emotional state.
Explore the engineering
Image input
A facial image is prepared for the model. Consistent input formatting helps the network learn from comparable data.
Interactive explanation of a classification pipeline. No images are analysed or uploaded by this diagram.
Let’s talk about this project.
I’m happy to walk through the design decisions, challenges and what I would improve next.
Contact Peter