The Memory Efficiency in a Receptionist Robot's Face Recognition System Using LBPH Algorithm

Abstract

This research aims to develop a memory-efficient face recognition system for a receptionist robot using the Local Binary Patterns Histogram (LBPH) algorithm. Given the computational limitations of the Raspberry Pi, the system utilizes optimization techniques including grayscale conversion, noise reduction, and contrast adjustment to enhance processing efficiency. Testing demonstrates that the face recognition accuracy achieves 80.5% to 85.5% in offline mode, and 72% to 81% in real-time mode, with variations due to lighting conditions and facial expressions. The robot's servo motors exhibit a response time between 1.945 and 3.561 seconds, enabling responsive and interactive user engagement. The results suggest practical benefits for deploying face recognition in resource-constrained environments, enhancing the efficiency of robotic receptionist applications.