Comparative Analysis of Face Recognition Algorithms: Evaluating LBPH and FaceNet with Haar Cascade

Facial recognition is a widely utilized technology in numerous industries, such as security, retail, delivery, and criminal detection. However, it encounters significant challenges when dealing with low-resolution images and videos. To achieve accurate facial recognition, researchers have explored various techniques, including LBPH, Face++, and ReKognition. Despite advancements, there remains considerable room for improving recognition accuracy. LBPH, a feature-based method, demonstrates remarkable proficiency in recognizing faces from diverse viewpoints. In contrast, deep learning approaches like FaceNet and Haar Cascade have emerged as prominent techniques. Haar Cascade specifically focuses on face recognition. This study evaluates the performance of LBPH face recognition system in comparison with a combined approach employing FaceNet and Haar Cascade using real-time video data.