An Intelligent Mobile Surveillance Robot with Real-Time Face Recognition, Emotion Detection, and Autonomous Navigation

This paper presents the design, implementation, and experimental evaluation of an intelligentmobile surveillance robot built around a RaspberryPi 4 Model B and an Arduino Uno R3 co-processingarchitecture. The system integrates real time computervision with autonomous navigation to deliver a capableand cost effective security platform. Face detection relieson the Haar Cascade algorithm operating on live cameraframes; recognised individuals are identified throughFaceNet generated embeddings compared via cosinedistance, while DeepFace supplies concurrent emotionanalysis. An HC-SR04 ultrasonic sensor mounted ona servo motor provides 180-degree obstacle scanningto prevent collisions during autonomous patrol. Theoperator interacts with the robot through a Flask basedweb dashboard that streams live video, displays recognition outcomes, and accepts manual control commandsover HTTP. Alternative low latency input is availablethrough a Bluetooth module and an infrared remote.Experimental testing on a three class dataset yieldedan overall classification accuracy of 73.33 percent, withthe unknown face rejection rate reaching 70 percent,confirming adequate performance for indoor securitydeployment. The Scrum development methodologystructured work into five incremental sprints, enablingcontinuous integration and validation of each subsystembefore the next was introduced. Results highlight boththe feasibility of embedding AI driven surveillance inlow cost hardware and the specific accuracy trade offsthat arise when running deep learning on constraineddevices