An Experimental Analysis of Illumination, Pose, and Background Variations on Classical Face Detection and Recognition Performance

Due to their ease of use and computational effectiveness, traditional face recognition methods like Local Binary Patterns (LBP) and Principal Component Analysis (PCA) have been thoroughly researched conventional face recognition techniques such as Local Binary Patterns (LBP) and Principal Component Analysis (PCA) are widely used because they are simple to implement and computationally efficient. However, their performance in real-world situations is still limited. This paper presents an experimental study to examine how elements like changes in circumstances of lighting, variations in face pose, complex backgrounds influence the performance of conventional face recognition methods. Recognition accuracy is assessed using benchmark datasets representing controlled, semi-controlled, and unconstrained environments. The experimental results show that classical methods perform well in controlled conditions but their accuracy decreases significantly when illumination varies or when backgrounds become complex. These findings highlight the limitations of manually designed feature-based approaches and indicate the need for more advanced representation learning methods for reliable biometric recognition.