Appling XGBoost for Advanced Face Recognition System

Face recognition technology has significantly advanced and become essential in various applications, including security systems, human-computer interaction, and biometric authentication. This work introduces an effective face Recognition for recognizing a person from facial images. This work utilizes state-of-the-art preprocessing and augmentation techniques, histogram equalization, Gaussian blur, and Canny edge detection in enhancing quality and diversity within the data. These will ensure that all facial features are captured, including those extracted by Deep Face, HOG, LBP, and Gabor filters. Some of the machine learning methods applied and tested on this CAS-PEAL dataset include XGBoost, Decision Tree, and Random Forest. Of these, the best performance was by XGBoost, at near perfection. The outcomes hereby show that an efficient fusion of multi-feature extraction approaches with the recent machine learning methods improves the reliability of face recognition against every change in environmental conditions.