Face Binary Classification Using Bag of Visual Words Model

I have conducted research and implementation of binary face classification code based on the bag of visual words (BoVW) model. The implementation utilizes Probabilistic Latent Semantic Analysis (PLSA) along with two additional classification modes to enhance classification accuracy and performance. The code incorporates optimized feature extraction techniques using SIFT (Scale-Invariant Feature Transform) descriptors and implements k-means clustering for visual vocabulary creation. For feature matching, I've employed histogram intersection and chi-square distance metrics to improve the model's ability to capture critical facial features for classification tasks. The model architecture includes a pipeline for preprocessing facial images, extracting local features, constructing visual dictionaries, and generating feature histograms. Additional improvements involve adaptive parameter tuning for different facial datasets and application scenarios, including handling variations in lighting conditions and facial expressions. Through these enhancements, the binary face classification code achieves not only higher accuracy but also demonstrates improved robustness and scalability. The implementation includes cross-validation mechanisms and performance evaluation metrics to ensure reliability across diverse datasets. I believe this code provides valuable support for face-related research and practical applications, offering a solid foundation for further developments in computer vision and pattern recognition.