# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.1 Physical Biometrics
Unlike iris recognition, their method did not use high-frequency features of the image; thus, it offered fast and efficient recognition with minimal effect on image quality. It showed excellent performance, with an EER of 6.83%. [PERSON] et al. [104, 105] presented a proof-of-concept to target the segmentation of off-axis iris images taken by frontal embedded cameras in HMDs. Particularly, they used advanced data augmentation techniques on publicly available iris datasets and designed a novel Convolutional Neural Network (CNN) architecture with minimal complexity for embedded devices' deployment. The segmentation results of their method were compared with advanced segmentation models and showed great performance for both frontal and off-axis iris regions, despite the lightweight nature of the suggested model. Thus, it is a suitable choice for deployment on HMDs. In [106, 107], [PERSON] et al. addressed the issue of anonymizing iris images during eye tracking in HMDs. The authors suggested a hardware-based optical defocus solution to conceal the iris biometric from the stream of eye-tracking images. Both their pilot study with 5 participants and the study with 15 participants, respectively, produced an average Correct Recognition Rate (CRR) of 0% and 7% compared to 91% and 79% before defocus was applied. [PERSON] et al. [108] were among the first researchers who tried to solve the problem of user authentication in wearable smart glasses. They designed an innovative iris recognition mechanism and proposed an efficient and accurate iris segmentation algorithm to be used on smart glasses. The accuracy of their iris recognition system on smart glasses reached 100%. Many researchers have investigated other physical biometrics besides the iris. [PERSON] et al. [109] proposed SoundLock, an innovative biometric user authentication system for VR systems based on auditory-pupillary responses. SoundLock authenticates users by using carefully crafted features extracted from variations in pupil size captured by an integrated eye tracker in response to auditory stimuli presented via the VR headset. The response is compared to the template created at the enrollment stage to verify the user's legitimacy. The solution offers an essential direction for choosing efficient auditory stimuli and establishing their appropriate durations. Through comprehensive in-field studies, the authors demonstrated that their proof-of-concept accuracy surpasses the performance of cutting-edge biometric systems with an EER as low as 1.5% and is well-liked by the study participants. Similar work based on the core idea of using pupil light reflex (PLR) for biometric authentication by [PERSON] et al. [110] confirmed the feasibility of the PLR signal for authentication through experiments. Their authentication experiments were designed based on three different authentication light intensities. The results of their authentication system turned out promising and achieved quick and effective authentication with 99% accuracy, showing that the system can be integrated into a more secure biometric authentication system. To the best of our knowledge, this work on PLR for smart helmet device authentication is original. [PERSON] et al. [111] proposed EarEcho, a novel acoustic biometric authentication scheme that takes advantage of the unique characteristics of in-ear sound. They validated that user authentication on wearable devices can be achieved using acoustic features extracted from audio emitted from an integrated earbud speaker combined with the reflected echoes of those audios recorded by a microphone. Their implemented and tested proof-of-concept on 20 participants attained a 97.57% precision rate for continuous authentication. EarEcho also showed that it is stable and resilient enough to handle ambiguities related to background noise, body movements, and sound pressure levels. [PERSON] et al. [112, 113] explored the potential practicality of neurofeedback-based biometrics for head wearables.