A Resource-Efficient Binary CNN Implementation for Enabling Contactless IoT Authentication

Access control is already an integral part of the Internet of Things (IoT) to prevent unauthorized use of systems. However, in the post-pandemic world, contactless authentication is desired for multi-user in-person systems. Biometric key-based hardware obfuscation can enable user-specific access control for IoT devices with added protection against piracy, reverse engineering, and hardware tampering. Biometric key-based unlocking of devices relies on various feature generation and classification tasks, for which convolutional neural networks (CNN) have demonstrated state-of-the-art effectiveness. In this regard, CNN-based contactless biometric template submission (e.g., face) can be a potential candidate. However, for resource-constrained devices (i.e., IoT), especially those with limited hardware capabilities, might struggle to efficiently execute CNN-based models due to their intensive memory access patterns, operational delays, communication bandwidth, and power consumption. Exploiting the recent advancements in information flow theory in neural networks and binary features, in this paper, we propose a CNN-based biometric system where binary weights and activations are used to generate compact yet, meaningful binary biometric features to enable computation on resource-constrained edge devices. We implement the proposed framework and a conventional CNN on a face dataset in an FPGA as a proof-of-concept and obtain a classification accuracy of 96