Indoor Space Authentication by ISS-based Keypoint Extraction from 3D Point Clouds
This work provides a more practical and privacy-preserving method for indoor space authentication, which could be beneficial for device-independent authentication and account recovery mechanisms.
This paper proposes ISS-RegAuth, a lightweight framework for indoor space authentication using LiDAR captures of personal rooms. By using only 1-2% of Intrinsic Shape Signatures (ISS) keypoints, the method reduces the equal-error rate from 0.02 to 0.00, cuts processing time by 20%, and lowers transmitted data to 2.2% of the original on 100 ARKitScenes pairs.
We propose ISS-RegAuth, a lightweight indoor space authentication framework that authenticates a user by comparing LiDAR captures of personal rooms. Prior work processes every point in the cloud, where planar surfaces such as walls and floors dominate similarity calculations, causing latency and potential privacy exposure. In contrast, ISS-RegAuth retains only 1-2\% of Intrinsic Shape Signatures (ISS) keypoints, computes their Fast Point Feature Histograms, and performs RANSAC and ICP on this sparse set. On 100 ARKitScenes pairs, this approach reduces the equal-error rate from 0.02 to 0.00, cuts processing time by 20\%, and lowers transmitted data to 2.2\% of the original. These results show that keypoint-based sparse representation can make privacy-preserving, edge-deployable indoor space authentication practical. As an early step, this work opens a path toward device-independent authentication and account-recovery mechanisms that rely on users' physical environments.