# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.2 Behavioral Biometrics
Normalization, spatial, and smoothing techniques were used to preprocess input data. Similar study settings as [154; 156] and DL algorithm architecture were used in their first model to perform matching, while an N-class Fully Convolutional Networks (FCN) architecture similar to the work in [157; 158] was used in their second model to perform classification. Both models take position, orientation, and displacement vector features from input and enrollment trajectories and return a match distance between both trajectories. The lowest distance represents an identification match, and a positive match between the trajectories distance and a threshold represents successful authentication. They compared their method to different baseline methods, and their results reached a 2.08% improvement. Their identification results showed success for 36 users out of 42. They showed their interest in creating and investigating VR applications that represent daily activities such as office visits, doctor visits, etc. In addition, [PERSON] et al. [159] performed a temporal effect study on behavioral authentication. They used the same model used in [154] and data from [152; 153; 154] to study the relationship between enrollment and input trajectories over short, medium, and long timescales. Their results suggested that both short and medium timescales have an insignificant impact on VR behavior for day-to-day tasks. However, data collected during a period of 8 to 17 months showed that long timescales result in varying user behaviors. Their best results' performance in the long timescales category was achieved by using long timescales data to train the model since it contains both the baseline behavior and the changed behavioral data. To amend the issue of data security in XR systems, [PERSON] et al. [135] proposed Gaitlock, an AR gait authentication mechanism based on users' gait signatures. The study was founded on the tasks of short indoor walks and arbitrary outdoor walks while wearing GG. The gait data was collected from 20 subjects using the IMU device embedded in the HMD, and the noisy head movements data was filtered. The authors custom-built a model using DTW and Sparse Representation Classification (SRC) techniques for authentication and compared their evaluation results with the best alternative techniques. A total of 98% accuracy was achieved, and it outperformed its rivals. They additionally evaluated the performance of Gaitlock on zero-effort and mimicry attacks; the prototype scored 0.021 vs. 0.029 for zero-effort and mimicry attacks, respectively. Thus, we conclude that mimicking the user's gait pattern increases the risk of the attacker being authenticated. Despite evaluating Gait Lock using only GG, they noted the possibility of transporting it to a VR environment. They highlighted their future interest in considering multimodal gait tasks, such as running, since they are ruled by more complicated constraints. [PERSON] et al. [160] looked into body interaction usability in VR. They established their work on the importance of skeleton-tracking technology in motion recognition and proposed a hybrid approach using skeleton-tracking techniques and depth cameras as a new interaction modality in VR to replace HHC. They pointed out their future plans to incorporate speech recognition techniques into their future work. Implementing novel interaction methods lays the foundation for practical authentication solutions. [PERSON] et al. [161] explored user identification in VR using gaze and head movement dynamics instead of body size, proportions, and position of users. In an experiment with 43 volunteers watching a 360\({}^{\circ}\) video, the authors collected gaze and head movement datasets. A statistical-based classifier and a DL-based classifier were used to test the performance of their approach. Accuracy results revealed 74.4% with the Gaussian Mixture Models (GMM) classifier and 80.1% with a CNN classifier.