Advances in Face Recognition: A Comprehensive Review of Feature Extraction and Dataset Evaluation

Face recognition has become a major research area due to the rapid growth of intelligent software applications. However, reliable face identification remains challenging because human facial features vary significantly under different conditions. Originating from pattern recognition, image processing, and computer vision, modern face recognition continues to advance through new algorithms and learning-based approaches. This paper describes and analyzes the existing literature regarding facial recognition and surveillance systems. It describes and explains the principles underlying facial recognition and surveillance in a general sense and analyzes the most significant application domains. Furthermore, it describes and analyzes the most relevant and widely used benchmark datasets that can be used to measure the recognition and surveillance performance of such systems. We also discuss and analyze the most relevant and significant issues related to existing systems and datasets. Two primary feature extraction categories are discussed in detail, followed by a comparison of appearance-based, model-based, and hybrid methods. Important components such as feature selection, distance measures, classification techniques, and evaluation protocols are also reviewed. Finally, the review summarizes current challenges and emerging research trends, offering insights into future directions for developing more accurate, robust, and practical face recognition systems.