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The increasing number of online courses and programs available worldwide has elevated the importance of reliable online exam proctoring. The typical proctoring process relies on webcam surveillance. However, this traditional method of proctoring is vulnerable to numerous types of face occlusions used for religious reasons or otherwise. We present a robust biometric authentication framework that combines advanced eye and face recognition, resulting in a much better live proctoring system that is further augmented by including fingerprinting. We have used cutting-edge deep learning techniques, specifically the Siamese network for fingerprint analysis and a ResNet-based eye recognition model tested with and without Gabor filters. Furthermore, our system offers much better performance compared to previously existing models. Notably, our system maintains high accuracy, approximately 98.04% for custom eye recognition, 99.01% for publicly available labeled faces dataset, 82% for niqab dataset and 87.04% for publicly available fingerprint dataset. Moreover, our model demonstrates a 10–20% improvement in face recognition under occlusion. Our solution is highly effective for not only online proctoring but also allows use in other similar situations, such as employee authentication for remote presence verification. Our system supports scenarios involving partial occlusions, such as masks and sunglasses, to full occlusions with veils, without requiring any additional hardware.