# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.2 Behavioral Biometrics
Eye tracking gaze positional data & & & \\ \cline{2-7}  & \multirow{9}{*}{**[164]**} & \multirow{9}{*}{2023} & \(\text{XR-Embedding }^{*}\) & \(\text{GRU}\) & _Playing the VR (name “Hall-Life: Aky”_ & & \\  & & XR-Classification \({}^{*}\) & & \(P\) & Positional (v,y,z) and orientational & & \\  & & & & (quaternion, x,y,z,w) coordinates from the HMD and both HHC (21 features) & & & \\ \cline{2-7}  & \multirow{9}{*}{**[165]**} & \multirow{9}{*}{2021} & \(\text{PositionF-within-kN}\) & & & & \\  & & & PositionF-within-RF & & & & \\  & & & PositionF-within-GBM & & & & \\  & &  
Electrical Bio-Signals  
The integration of bio-signals in XR equipment may significantly improve security, privacy, and authentication results on such devices. Compared to traditional physiological traits' data, bio-signals are more difficult to steal or forge due to their continuity. In recent years, research on bio-signals in XR has received a lot of attention. We selected papers that comprehensively include schemes and mechanisms that use bio-signals methods for XR user authentication. [PERSON] et al. [166] investigated the use of Electromyography (EMG) sensor-based controllers over hand-based controllers in VR. The authors collected EMG and IMU data during an experiment with five subjects typing the word \"password\" while wearing a MYO. They also used different deep-learning methods to train and evaluate the collected dataset. The accuracy of their 1D-CNN network reached 96.45%, which demonstrates the capability of sensor-based devices such as the armband to identify their users. They argued that despite the MYO not being in production anymore, similar devices hold promising solutions for authentication. They pointed out that their future work will include more participants, up to 100, and extensive usability feedback. In [167], [PERSON] et al. proposed PerAE, an autoencoder-based Electrocardiogram Identity Recognition system (EIR). PerAE has a dynamic updating mechanism consisting of an attention-Memory-Autoencoder network (MemAE) trained using the heartbeat data of only one user. It requires just training or updating the autoencoder rather than a full deep classification model, making PerAE efficient, adaptable, and maintainable. To evaluate PerAE, the authors collected experiments on three open-source ECG (Electrocardiogram) datasets, and the results show a balance between recognition accuracy and training efficiency. The recognition accuracy for a user reached 93.3%. The authors pointed out their plan to improve the accuracy of the model by using multimodal data in their future research. To overcome the security issues of traditional knowledge-based authentication and the high error rate and low stability of eye gaze authentication methods, [PERSON] et al. [168] explored the Human Visual System (HVS) as a whole to construct a VR authentication system. They presented OcuLock, an EOG-empowered HVS sensing mechanism that takes into consideration the eyelid, extraocular muscles, cells, and surrounding nerves. In their system, three stimuli (fixed route, city street, and illusion) in 3D form were presented to the user, and EOG (Electrooculogram) data were collected and filtered. Then, many machine learning models, such as SVMs and k-NN, were used for training, record comparison, and recognition. Their user experiments on 70 participants show resistance to impersonation and statistical attacks with an EER as low as 3.55% and 4.97%, respectively. The results of a 2-month longitudinal study also reveal that OcuLock maintains stable performance and is favored by users. [PERSON] et al. [169] looked into the effect of VR presence on brain portions and whether the Electroencephalogram (EEG) signal collected during that presence can be used for user authentication. In their study, unique EEG signals were collected from 32 participants watching a VR video. Then, those signals were processed using three feature extraction techniques and machine learning classification models. The best-obtained accuracy result for this dataset was 80.91%.