# 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 pointed out that they excluded the contribution of physical characteristics for the purpose of avoiding classification confusion between users with similar physical measurements within the class sample. They also highlighted that, for the purposes of ecological validity and identification robustness, using multi-session videos recorded days apart instead of one video session would have been more suitable. Based on the uniqueness of eye gaze movements as a biometric trait, [PERSON] et al. [156] proposed a user identification system by using fixations, smooth pursuit, saccadic movement, and head orientation as dependent reactions to visual stimuli. Following the VR design presented in [127], the authors built a VR stimulus scene represented by a counter-clockwise moving sphere on an elliptical path and collected eye gaze and head rotation data from a small pool of 11 individuals using an HTC-V HMD modified with additional infrared cameras. They used a 1-NN ML model and eight mainly CNN-based DL models to classify the users. The highest identification accuracy results in both classifier categories reached 75% vs. 100% for the ML and DL models, respectively. In [162], [PERSON] et al. introduced an approach for user identification from among a set of HMD users using blinking and head movement data. They used a series of images as stimuli to collect data from 20 subjects, utilizing infrared, accelerometer, and gyroscope sensors integrated into the HMD. They extracted 162 features; blinking features were classified into five classes (IR peaks, rising IR, falling IR, IR peak interval, and IR floor); head-movement features were classified into three categories (Gyroscope peaks, accelerometer peaks, and movement time); then the features were reduced to 96 features. These features were used to examine many ML classifiers, and an RF classifier with 100 trees was selected and achieved 94% user identification accuracy. Work by [PERSON] et al. [136] looked into the application of behavioral biometrics and body motion to user identification in VR. They implemented their study based on tasks such as typing, walking, grabbing, and pointing by utilizing head and hands data collected using an HTC-V HMD equipped with a Pupil Lab Eye Tracker (PLab). They used RF and SVM classifiers and high-level features like max, min, mean, and standard deviation derived from low-level features, namely distance, motion, rotation, and motion. Their most accurate results achieved only 44.44% within the task of typing and the feature set of distance. The head motion feature set scored second best among feature sets overall, while the head movement feature set was more suitable for the task of walking. For the hand movement feature set, the pointing task scored better than the grabbing task. Their low score results warrant the need for further research and investigation. [PERSON] et al. [163] presented BioMove, a VR user identification study of 15 participants performing controlled tasks such as grabbing, rotating, and dropping tasks. They particularly extracted eye gaze, head, and hand positional (elevating, string, surging) and rotational (rolling, pitching, yawing) movement data patterns and dimensionally reduced the features for efficiency purposes. Using ML classification techniques, mainly k-NN, both user identification and authentication were investigated. Two identification models were implemented, one for task identification and another for participant identification. They concluded that the k-NN model had the best user identification accuracy of 98.6% out of all classifiers. The authentication results showed a 0.0032% false positive rate and a 1.3% false negative rate, making the system biased toward rejecting valid users instead of giving invalid users access to the system. The authors argued that the authentication results can be improved, although it is out of the scope of their research.