My first AI project for estimating age and recognizing facial expressions. UTKFace and FER-2013 datasets were used as well as some popular Python libraries like OpenCV, TensorFlow, and Keras.
Tackling the FER-2013 dataset using different DL models while focusing more on a data centric approach.
Multi-Class Deep facial emotion classification (vision)
A 5-layer CNN model for facial emotion recognition trained on FER-2013. Achieved 76% validation and 63% test accuracy using data augmentation. Key features include convolutional layers, max pooling, and dropout. Suitable for human-computer interaction applications.
Facial Emotion Recognition using the FER-2013 dataset – preprocesses, normalizes, and visualizes facial expression data for machine learning applications.
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