# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 6 Discussions and Future Research Directions  
This section discusses the evaluation of the different XR authentication schemes and their advantages and disadvantages. After studying 197 publications, we concluded the following. Knowledge-based authentication schemes highlighted the evolution of password-based authentication methods in XR. Mainly, 40% of those schemes focused on textual password input modalities, 45% focused on graphical password input methods, 9% addressed haptic password techniques, and 6% presented semantic password solutions. In the usability and security evaluations, 53% of those schemes were analyzed considering both usability and security aspects. A total of 25% of the schemes were investigated regarding their usability only, and only 3% were investigated regarding security only. A total of 21% of the schemes were not evaluated in terms of usability or security. This shows the absence of a standard evaluation bed, making it hard to compare the efficiency of the different schemes. The different usability aspects addressed in the usability studies were effectiveness (input accuracy and authentication time) and perceived usability (satisfaction and memorability) using in-lab studies, surveys, and questionnaires. PIN-based schemes scored highly in terms of effectiveness in comparison with other modalities, such as graphical or haptic. Users' familiarity with PIN-based authentication systems from using smartphones and laptops might be the reason for the efficiency of PIN-based schemes. However, this needs to be investigated more. Speech-based schemes scored the lowest in terms of authentication time. The security aspect addressed in most schemes was resistance against shoulder surfing attacks. The resistance of textual-based and graphical-based schemes against observation and recording attacks depends on the randomization of the password or the input modality. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Concept_ & **Ref. ** & **Year** & **Scheme** & **Interaction Method** & **HMD** & **US** & **SS** & **A\% (ERR)** \\ \hline \hline \multirow{4}{*}{
\begin{tabular}{c} **M-2** \\ **M-2** \\ **M-3** \\ **  
Randomized schemes showed higher resistance than non-randomized ones. Schemes using eye gaze as an input modality were more resistant than schemes using traditional input modalities such as HHC. The proposed knowledge-based schemes showed advancements in usability and resistance to observation attacks. The main advantages of these schemes are users' familiarity and unneeded requirements for personal data. However, challenges such as authentication time, input modality preferences, and resistance to various types of attacks persist, emphasizing the need for ongoing research in XR authentication techniques. Biometric-based authentication schemes cover a wide range of methods and techniques. Physiological-based schemes represent 34% of the proposed work. Behavioral-based schemes represent the rest, with 43% devoted to biomechanical-based schemes, 15% dedicated to eye-tracking-based schemes, and only 6% for bio-signal-based schemes. All biometrical-based schemes required machine learning models ranging from statistical to deep learning techniques for implementation. Physiological biometrics offered significant improvements over knowledge-based methods in terms of usability and accessibility. However, they compromised user privacy and fell short in terms of cost, accuracy, and authentication time. Behavioral biometrics schemes generally offered fewer promising usability advantages compared to physiological biometrics schemes. One key distinction is that while some behavioral biometrics require minimal effort, such as eye-tracking-based ones, others demand more active and explicit actions, like biomechanical-based ones. For security, biomechanical-based biometrics lack resilience to observation attacks, a key strength of physiological biometrics, while eye-tracking-based biometrics seem more resilient to observation attacks.