# 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 Sensors  
For decades, eye tracking technology has been available on desktop displays and smartphones. Several technologies that enable eye tracking on XR HMDs have been recently created. Hence, work on eye tracking in HMDs has increased and expanded. Many researchers have investigated the use of eye-tracking technologies for authentication purposes on HMDs. [PERSON] et al. (2016); [PERSON] et al. (2017); [PERSON] et al. (2018); [PERSON] et al. (2019); [PERSON] et al. (2020) are among those who did extensive work in this area. Firstly, in [127], they implemented an eye movement VR authentication framework  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Concept** & **Ref. ** & **Year** & **Scheme Name** & **Classifier** & **Features** & **Equip. ** & **Sample** & **Av. (_LEER_)** \\ \hline \multirow{8}{*}{**\(\{\}  
using a previously proposed framework for an ocular biometric system. For optimization purposes, they used focused rendering techniques in the stimuli to collect the eye-tracking data, and only low-frequency signals were considered. Many statistical methods were used for feature extraction and matching. They pointed out many difficulties in terms of calibration issues, the required number of stimuli, and 3D eye movement data classification. They demonstrated their future intent to test their framework on a larger pool of data using complex 3D eye movement features and to compare different ML classifiers in terms of authentication results. Next, they realized their intent, elaborated on their previous work in [126], and introduced a novel 2D and 3D eye movement dataset. The dataset was collected in a VR environment with over 400 participants. They used both statistical and ML models to evaluate the suitability of the dataset for authentication. Their results were compared with results from an open-source dataset and showed worse performance. The authors argued that the poor performance might be linked to the way features were extracted and classified. They showed their interest in collecting more dataset recordings, using more features, and performing a longitudinal study in their future research. Furthermore, in [124,125], the authors proposed a lightweight and easy-to-train DenseNet-based architecture for end-to-end eye movement authentication. They trained and evaluated their model using the open-source eye movement dataset (GazeBase) collected from 322 subjects using the EyeLink 1000 eye tracker (EL1000) over a 37-month period. Data recordings from only 59 participants were used to verify and test the model across several eye-tracking tasks. The authentication results achieved a 3.66% EER. They also attempted to reach the level of spatial precision that exists in current VR HMDs such as the HTC Vive Pro (HTC-VP) by using artificial degradation techniques; however, their technique's performance dropped significantly. Thus, they left the issue for future studies on eye tracking to design proper methods for artificially degrading high-quality signals to imitate low-quality signals. Their technique was compared with other state-of-the-art techniques using different eye movement tasks for training and validation and other open-source datasets such as JuDo 1000 (which contains recordings of 150 subjects captured using similar equipment to GazeBase). To the best of our knowledge, their technique is the first to reach a level of authentication performance suitable for practical applications. Finally, in [123], the authors employed the previously proposed Deep Learning (DL) CNN architecture [124] and evaluated the performance of their model through a user study by collecting data from 5 participants using the HTC-VP VR headset. Their verification results from the user study revealed an EER of 0.2%. To achieve better outcomes, they suggested the use of larger VR datasets in future work. [PERSON] et al.