Leveraging Profiling for Facial Recognition Optimization

This research presents an advanced approach to facial recognition systems through a combination of incremental supervised learning and histogram-based feature extraction. Building upon prior work, the study aims to optimize recognition accuracy while minimizing computation time. The proposed methodology employs the Simplified Fuzzy ARTMAP neural network for classification, enhancing its efficiency by leveraging a simplified version of the Fuzzy ARTMAP algorithm. Local Binary Patterns (LBP) are utilized for feature extraction, effectively capturing intricate facial textures. A comprehensive profiling analysis is conducted to ensure optimal performance, identifying time-intensive segments for strategic enhancements. Experimental validation is conducted on three open-source face databases, showcasing exceptional recognition rates across diverse scenarios. This approach proves particularly effective when the training data consists of subjects with limited facial expression variance, such as the AT&T database. Additionally, the application demonstrates superior outcomes when the datasets maintain an ordered distribution. These findings collectively underscore the robustness and promise of the proposed methodology for enhancing facial recognition accuracy in various real-world contexts, making meaningful contributions to the domain of facial authentication systems.