Incremental Learning-Enhanced Ensemble Convolutional Neural Network for Robust Pose-Invariant Face Recognition

In the dynamic realm of face recognition, where challenges such as pose variations, illumination changes, and expression nuances abound, this research embarks on a novel journey toward enhancing recognition robustness. Herein lies an amalgamation of innovative methodologies aimed at transcending the boundaries of traditional face recognition paradigms. At the forefront, an Adaptive Morphological Bilateral Filtering (AMBF) technique emerges, poised to revolutionize image preprocessing. AMBF stands as a beacon of adaptability, seamlessly sharpening blurry facial images while mitigating the risks of introducing undesirable artifacts. Its arsenal of morphological operations, carefully orchestrated to uplift image quality, presents a promising solution to the perennial challenge of recognizing faces under adverse conditions. Complementing this pioneering filtering approach is the integration of an Ensemble Convolutional Neural Network (ECNN), an avant-garde ensemble learning architecture.