Smart Inventory Access Monitoring System (SIAMS) using Embedded System with Face Recognition

Abstract

In this paper, we present a system called Smart Inventory Access Monitoring System (SIAMS) that integrates an embedded system with face recognition into an inventory system. It is developed to prevent theft in warehouses from authorized staff. The embedded system is attached with an RGB camera and deployed three software modules: image capturing, face detection, and face recognition. The face detection module sends detected face images to the face recognition module to identify a person as the person’s name or unknown class using a deep learning approach. The system achieved competitive accuracy by performing standard evaluation metrics for face detection and recognition. The inventory system that was developed will receive data via TCP/IP socket communication to log access history. The retrieved information can be used to investigate an unusual situation. The system can be improved with object detection and person tracking system to detect theft in real-time.