This study evaluates the performance of the Viola Jones and YOLOv3 algorithms for facial recognition under different conditions and highlights their strengths and weaknesses. Analysis focusses on facial emotions, angle recognition, lighting, and the effects of hidden facial features. YOLOv3 outperformed the Viola-Jones algorithm in angle-based recognition with more robustness. Both algorithms performed exceptionally well in different lighting conditions, with 100% recognition rates in artificial, natural, high-contrast, and dark surroundings. This shows that they are highly adaptive to changing lighting conditions. When individual facial characteristics, such as the forehead or eyes, were concealed, the Viola-Jones algorithm showed excellent reliability. When the nose and eyes were concealed, however, its performance dropped to 77%. YOLOv3, on the other hand, consistently achieved a 100% recognition rate, indicating that it handled inadequate facial data better, even in scenarios where multiple significant attributes were concealed. Both algorithms proven their resistance to dynamic face changes by achieving 100% recognition rates over a wide range of expressions and proving that facial expressions had no effect on their recognition accuracy. These algorithms should be improved in the future for extreme angles and partial occlusions, and their integration with other recognition methods should be investigated.