# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.2 Behavioral Biometrics
They used an RF classifier to implement their approach. Their best identification accuracy results reached 95% vs. 88% for the bi-manual keyboard AR and VR prototypes, respectively. They finally used the System Usability Scale (SUS) questionnaire and the NASA Raw Task Load Index (NASA Raw-TLX) questionnaire to assess the usability of their prototypes. From the participants' feedback, the VR prototype scored higher in terms of usability and interaction preference. [PERSON] et al. [141] implemented a touch-based continuous authentication system for smart wearables. Considering the accuracy and computational load of wearable devices, their work assesses the portability of touch-based authentication mechanisms, from smartphones to GG. They modeled the touch gestures in a novel way and extracted new touch features. The authors utilized an SVM classifier with a Gaussian Kernel-Radial Basis Function (GK-RBF) and designed a custom-built Chebyshev classifier (Chebyshev) based on Chebyshev concentration inequality to perform classification. Their motivation to use a new Chebyshev classifier is to support prior works' findings on touch-based smartphone authentication, indicating that using a block of successive touch gestures is more effective than a single gesture. The Chebyshev showed a 99% classification accuracy, nearly comparable to SVM results. They suggested continuously adjusting and training the classifier with recent data to alleviate the effects of data drift on the classifier's accuracy. [PERSON] et al. [142] suggested FMHash, a user identification prototype using deep hashing of in-air handwriting. The framework generates a compressed binary hash code from in-air written ID strings that facilitates users' database indexing and searching. They first carried out a data collection experiment with 100 subjects who wrote an ID string and implemented their framework using CNN classifiers. The evaluation of the FMHash showed a precision of \(\geq\)99.5, making it a robust in-air handwriting identification solution for wearable devices. They juxtaposed their framework with another deep hashing framework used for image retrieval and highlighted that their framework is better optimized. The authors pointed out their future interest in investigating the prototype's long-term performance through a longitudinal study. [PERSON] et al. [143] presented an eye-free interaction approach seeking to address the non-secure process of simultaneously performing authentication and task execution. They proposed a hand-motion VR authentication system without eye motion involved to shift attention from concentrating on authentication to performing other tasks. They collected data from 10 participants who were asked to draw a 3D trajectory using their dominant hand's HHC. Their work-in-progress accomplished the data collection pilot study and the feature extraction phase, and the authors pointed out their future plans to implement the SVM classifier to validate their approach. [PERSON] et al. [144] examined the effectiveness of voice commands and touch gestures for continuous user authentication on wearable devices, utilizing the GG case. They proposed GlassGuard, a non-invasive authentication mechanism that uses seven simple interaction modalities (touch gestures (single-tap, swipe forward, swipe backward, swipe down, two-finger swipe forward, and two-finger swipe backward) and voice commands). GlassGuard is composed of five modules (event monitor, feature extraction, classifiers, aggregator, and power control). The event monitor monitors the type of event (touch or voice) and extracts the data, then forwards the data to the feature extraction module. The classifiers module contains 7 classifiers for each interaction modality, and the classifier corresponding to the event makes an independent decision if the event features belong to the owner. The aggregator is custom-built based on Threshold Random Walking (TRW), a spam detection algorithm, and it combines the classifier results to improve authentication accuracy.