Enhancing Electric Vehicle Security with Face Recognition: Implementation Using Raspberry Pi

Facial identification has emerged as a key research area due to its potential to enhance biometric security. This research proposes an advanced security system for electric vehicles (EVs) based on facial identification, implemented using Raspberry Pi. The system comprises two main modules: Face Detection and Face Recognition. For face detection, the researchers propose using the Viola-Jones algorithm, which leverages Haar-like features to detect and extract unique facial features, such as the eyes, nose, and mouth. MATLAB will be used as the development tool for this module. For face recognition, the proposed approach integrates Principal Component Analysis (PCA) with Support Vector Machine (SVM). PCA is used to extract the most relevant facial information and construct a computational model, while SVM enhances classification accuracy. The system's performance is evaluated using accuracy and the Receiver Operating Characteristic (ROC) curve. The accuracy of face recognition depends significantly on the quality of the training dataset. For this study, the AT&T dataset was selected due to its standard format, suitable for facial recognition training. The proposed system achieved a face recognition accuracy of 95%, demonstrating its effectiveness in enhancing EV security. This method promises reliable and advanced biometric protection for modern applications.