Face recognition is a vital biometric technology with growing applications in security, access control, and automation. Despite challenges such as lighting variation, facial expressions, pose angles, and aging effects, Support Vector Machine (SVM) has proven effective in modeling and classifying facial features due to its ability to construct optimal decision boundaries in high-dimensional spaces. In this study, SVM was applied to a face recognition system for smart lock operations, yielding strong performance metrics of 89.9% accuracy, 89.7% precision, 89.5% recall and an F1 score of 89.7%. These results demonstrate the capability of SVM to effectively manage facial variability while maintaining high accuracy in face recognition tasks.