Effective preprocessing techniques for improved facial recognition under variable conditions

Facial recognition systems are increasingly used across various applications; however, their performance often degrades in challenging conditions such as poor lighting and occlusions. Preprocessing techniques play a critical role in improving input image quality, enhancing feature extraction, and ultimately boosting recognition accuracy. This study evaluates advanced preprocessing methods, including edge detection using the Canny detector and illumination normalization through histogram equalization and gamma correction, which are integrated into a preprocessing pipeline. A detailed comparative analysis demonstrates significant recognition rate improvements under low-light and occluded scenarios, supported by quantitative evidence. Additionally, computational efficiency is evaluated, highlighting the applicability of these methods for large-scale and real-time systems. The results affirm that effective preprocessing strengthens the performance and reliability of facial recognition systems, making them suitable for real-world applications where conditions are often unpredictable.