Design of an Edge Computing–Based Security System with Multimodal Face and Voice Biometric Authentication

Abstrak

The increasing number of intrusion and unauthorized room access incidents necessitates the development of a security system that is more intelligent, responsive, and resistant to forgery. Conventional security systems or card-based identification methods are highly vulnerable to theft, duplication, and misuse, highlighting the need for biometric authentication that can operate independently without reliance on centralized servers. In this study, an edge computing approach is implemented by utilizing a Raspberry Pi as a local processing unit capable of performing real-time data processing without requiring an internet connection. A RaspiCam camera is employed to capture facial images, which are then detected using the YOLO (You Only Look Once) algorithm to achieve fast and efficient face detection. The detected faces are subsequently recognized using a Convolutional Neural Network (CNN). To further enhance security, a voice-based authentication stage is applied using the Mel-Frequency Cepstral Coefficients (MFCC) method to verify user identity. Experimental results demonstrate that the face recognition module achieves an accuracy of up to 96.5% under normal lighting conditions, while the voice recognition module reaches an accuracy of 94.7% in a quiet environment with a signal-to-noise ratio of 30 dB. Both authentication processes are executed sequentially with an average processing time of less than 500 ms, enabling real-time operation on an edge device. This approach provides a robust two-factor biometric authentication system that can be deployed without dependence on cloud-based services.