Complete Facial Expression Recognition System with PCA-Based Feature Extraction

This repository presents a complete facial expression recognition system that encompasses the entire pipeline from sample training to target identification. The core implementation utilizes PCA (Principal Component Analysis) for feature extraction, a widely adopted technique that effectively reduces data dimensionality while preserving critical facial expression patterns, thereby enhancing both recognition efficiency and accuracy. The system incorporates multiple preprocessing and postprocessing techniques including grayscale conversion, histogram equalization, Gaussian filtering, and morphological operations. These methods collectively improve image quality and mitigate noise interference, significantly boosting recognition stability and precision. Grayscale conversion simplifies color processing, while histogram equalization enhances contrast. Gaussian filtering reduces high-frequency noise, and morphological operations help refine facial feature structures. Through extensive experimental validation and optimization, this solution demonstrates robust performance suitable for various practical application scenarios. The implementation includes well-structured code modules for each processing stage, featuring clear function interfaces for data loading, preprocessing, PCA transformation, and classification. Key functions typically involve computeEigenfaces() for eigenvalue decomposition, normalizeFeatures() for data standardization, and trainClassifier() for model development using supervised learning algorithms. This proven framework offers a reliable and efficient approach to facial expression recognition, with modular code design that facilitates customization and extension for specific requirements.