# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.1 Physical Biometrics
For all we know, their work is the first to investigate using cancelable Event Related Potential (ERP) biometrics for secure user authentication. They addressed the issue of generating consistent brain reactions from complex visual stimuli and how to change those brain biometrics when the used credentials get divulged. They demonstrated the feasibility of their solution through a pilot study with an EER of 2.503%, thus validating the feasibility of using visual stimuli to generate brainwave responses for user authentication on smart headwear devices. They additionally performed longitudinal and cancelability studies to demonstrate the success and practicality of the suggested approach. [PERSON] et al. [114] presented SkullConduct, a biometric mechanism that uses the user's skull to conduct sound waves in order to authenticate users of SG. They reported on a user study with 10 subjects showing that the frequency resulting from the user's cranium is person-specific and stable despite moving the eyewear, which makes it a robust biometric. Their method brings to light biometric user authentication for SG with bone conduction technology, and it authenticates users with an accuracy of 97.0% and an EER of 6.9% without requiring explicit user input. [PERSON] et al. [115; 116] introduced a facial recognition authentication solution for a remote student proctoring virtual laboratory system. In their system, a Kinect camera scans student faces and captures their facial expressions and head movements to determine questionable behaviors. If suspicious behaviors are detected, the system records and analyzes more videos for further investigation. The authors concluded that the virtual prorotor system could provide high accuracy in detecting sketchy behavior and identifying and tracking users' faces. They also revealed that their future work on virtual lab proctoring would expand to not only facial recognition but also speech and gesture recognition. [PERSON] et al. [117] introduced a finger-vein-based recognition mechanism for VR HMDs. They argued that finger veins are hard to duplicate, which makes them a more secure physical authentication solution than other hand-based characteristics. They developed a model using CNNs and Anti-Aliasing Technique (AAT) and evaluated the model using three publicly available finger vein datasets. The results of their experiments surpassed those of well-established methods, with a high accuracy reaching 99.94% on the SDUMLA dataset (Shan-Dong University finger vein public dataset), making their model a reliable choice for authentication on VR headsets. [PERSON] et al. [118] addressed the issue of virtual worlds' platforms being equipped with only password-based authentication mechanisms by proposing a methodology to design a virtual world platform to implement a biometrical authentication mechanism. They used a 3D toolkit to build a complete virtual space and opted for a client-server configuration. In their proposal, the user provides their fingerprint at the client stage for enrollment and authentication purposes. Then, at the server stage, the user's identity is identified and confirmed to be genuine or not. [PERSON] et al. [119] explored the use of Electric Muscle Simulation (EMS) for biometric authentication due to its inter-subject variability. They engineered ElectricAuth, a mechanism that conducts users' involuntary finger movements by stimulating their forearm with electrical impulses. They showed that ElectricAuth is robust against impersonation attacks, replay attacks, and data breaches since it uses new challenges in each instance of authentication. ElectricAuth's longitudinal study results showed its stability under different humidity and muscle conditions. The authors stated that ElectricAuth would offer practical authentication for devices that use motion tracking, like VR headsets, and for users with motor impairments. Given that the propagation of vibration signals through the human head results in unique patterns between individuals, [PERSON] et al.