An Automatic Face Recognition Using Local Binary Pattern Histogram (LBPH) Algorithm

Face recognition is a rapidly advancing field with numerous applications in security, surveillance, biometrics, and human-computer interaction. This paper presents an innovative approach for automatic face recognition using the Local Binary Patterns Histograms (LBPH) algorithm. The LBPH algorithm is known for its simplicity, efficiency, and robustness in handling facial variations and occlusions. This research study conducts a comprehensive comparative analysis of the LBPH algorithm's performance against other popular face recognition methods, including Eigenfaces and Fisherfaces. It evaluates the algorithms on benchmark face databases and analyze their recognition accuracy, computational efficiency, and robustness to varying lighting conditions and facial expressions. The experimental results demonstrate that the LBPH-based algorithm achieves competitive performance, outperforming other methods in certain scenarios. Moreover, the algorithm discusses the strengths, limitations, and potential areas for improvement. The findings of this study contribute to the existing body of knowledge in face recognition and provide valuable insights for researchers and practitioners working in this domain.