Cognitive image authentication systems implement specialized behavioral and cognitive parameters for accurate and secure user verification. This paper presents a dual-Face cognitive Cloud image authentication framework for improved accuracy and secured cognitive biometric features. The current work addresses the endpoint weaknesses of malicious hacking and biometric systems. The MobileNetV2 model along with XOR segmentation and encryption safeguard the system’s real-time authentication. Essential features include optimizing facetime image and video over the handheld swiping devices to the cloud and the retention of authentication accuracy regardless of ageing, accessories, and vanity. Additionally, the performance of MobileNetV2 cognitive authentication and the rest of the systems were tested and analyzed. An experimental result comparison with the rest of the systems showed that the proposed MobileNetV2 based model achieved over 95.3% accuracy and a 91.11% F1 score for real-time and large-scale datasets respectively, outperforming the rest of the models including CNN, ResNet50, VGG19, hence able to work as a satisfactory cloud-based authentication. The proposed system showed response time for the XOR encryption method of 55ms to 75ms, which was also 63-68% faster than the proposed system using AES encryption. Cross-architecture performance evaluation with several deep learning systems confirmed the performance of MobileNetV2 as the best available for cloud scalable authentication. The proposed system offers a major cognitive biometrics advancement by providing a lightweight, efficient and robust authentication framework.