With advancements in artificial intelligence, face recognition technology has significantly improved in accuracy and reliability, yet concerns over privacy and data security persist. Currently, methods for addressing privacy issues focus on software and hardware levels, facing challenges in system power consumption, computational complexity, and recognition performance. We propose a novel privacy-preserving face recognition system that safeguards privacy at the optical level before signals reach the sensor. This approach employs a mask-encoded microlens array for optical convolution, effectively protecting privacy while enabling feature extraction for face recognition with a backend electronic neural network. Based on this passive optical convolution implementation under incoherent illumination, our system achieves superior spatial resolution, enhances light throughput, and improves recognition performance. An end-to-end training strategy with a dual cosine similarity-based loss function balances privacy protection and recognition performance. Our system demonstrates a recognition accuracy of 95.0% in simulation and 92.3% in physical experiments, validating its effectiveness and practical applicability.