# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.2 Behavioral Biometrics
The hierarchal architecture boosted the identification accuracy to 94.3% from 90.1% with only one layer. They additionally performed analysis about the impact of the metadata, such as country and type of device used when playing the game, etc. present in the BeatLeader dataset on the accuracy results. Also, an effect analysis of the telemetry data (static, motion, and context) on the system's entropy was performed. They finally expressed their future hope to use DL models to address similar issues. Many researchers investigated VR user authentication using full HMD data (headsets and controllers). Various works used ball throwing as their experimental task, where an imposter tries to mimic a genuine user. Initially, [PERSON] et al. [150] argued the uniqueness of biomechanical movement from one user to another and used the 3D trajectories of the dominant hand controller for user authentication in VR. In their study, users three a ball at a target, and their hand trajectories data was compared to the dataset of other users. Using the Symmetric Nearest Neighbor (S-NN) algorithm, their system was proven efficient, as its accuracy reached 92.86%. They pointed out their intention to include head trajectory data and to create more tasks in their future works, such as physically swinging a golf club or cognitively solving a puzzle. Using a similar case study and task as [PERSON] et al. [150], [PERSON] et al. [151] improved their authentication results by using pairwise relationships between 3D trajectories data from not only the dominant hand but also the recessive one and the head. They also used a 33-participant dataset, exceeding similar previous biometric works. Their mechanism was based on Perceptron Neural Networks (PNN) and reached 93.03% accuracy. They believed similar results could be achieved using other VR headsets, such as Oculus Rift (OR) and Samsung Gear VR (SGVR). Additionally, [PERSON] et al. [152] provided a real-time version of the [151] case study, and the difference lies in distinguishing between imposters within and outside the training set using a threshold-based method. The new number of trajectories used to train and test the model was N = 330 pairs. Moreover, [PERSON] et al. [153] made several contributions to prior work. They noted that VR users do not always use the same type of HMD and that changing the headset comes with high time consumption in terms of training to perform authentication. They were the pioneers of task-driven biometrics using different VR headsets (Oculus Quest (QQ), HTC Vive (HTC-V), and HTC Vive Cosmos (HTC-VC)) and the first to perform cross-system authentication. They collected a multi-system dataset using the previously mentioned headsets with 46 users, a number higher than prior studies by [150; 151; 152] (14 and 33 users, respectively). They also introduced linear and angular velocity features to prior work [150; 151; 152] and analyzed a significantly higher number of features compared to [151]. They restated the findings in [151] about the feasibility and impact of feature diversification on user authentication. Using the same study settings and dataset used in [153], and in order to improve their earlier work, [PERSON] et al. [154] suggested using a Siamese Neural Network (SNN) to perform across-systems authentication. For the purpose of avoiding user enrollment each time a user changes the VR device, they used an SNN to learn the distance metric between the headset/controller trajectories of the VR enrollment device and the trajectories of the new VR device. Their system performs identification by providing the user with a minimal distance and authentication by comparing the distance to a threshold. Their authentication findings indicate a 1.39% EER when QQ is used for enrollment and HTC-V for new use, with an average range of 1.38-3.86%. The identification accuracy results reached 98.53%. As per DL training requirements, [PERSON] et al. [155; 146] addressed the issue of using small datasets to perform authentication and identification using DL models.