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Facial Gesture Recognition Using PCA
For my freshman linear algebra final, I built a live facial gesture recognition system that detects smiles using fundamental linear algebra techniques. I started by capturing faces with a webcam and isolating them using Haar cascades, then represented each face as a high-dimensional matrix. Using matrix inversion and dot-product computations, I trained a model to distinguish smiling from neutral expressions. The project involved iteratively testing live predictions, adjusting thresholds, and refining feature representations to improve accuracy. This experience demonstrated how linear algebra can solve practical problems in computer vision and gave me hands-on exposure to applying theory in real-time systems.
