Exploring Eye Movement Biometrics in Real-World Activities: A Case Study of Wayfinding

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 8; p. 2949
• Main Authors: Liao, Hua, Zhao, Wendi, Zhang, Changbo, Dong, Weihua
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.04.2022; MDPI
• Subjects: Accuracy; Biometric identification; biometric identification and verification; Biometrics; Biometry; Case studies; Datasets; Eye Movements; Eye-Tracking Technology; fixation semantic features; Humans; Laboratories; mobile and wearable eye tracking; pedestrian navigation; Recognition, Psychology; Saccades; Smartphones; stimulus-independent biometric recognition; New York; Germany; Taiwan; biometric identification and verification; stimulus-independent biometric recognition; mobile and wearable eye tracking; fixation semantic features; pedestrian navigation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22082949

Eye movement biometrics can enable continuous verification for highly secure environments such as financial transactions and defense establishments, as well as a more personalized and tailored experience in gaze-based human–computer interactions. However, there are numerous challenges to recognizing people in real environments using eye movements, such as implicity and stimulus independence. In the instance of wayfinding, this research intends to investigate implicit and stimulus-independent eye movement biometrics in real-world situations. We collected 39 subjects’ eye movement data from real-world wayfinding experiments and derived five sets of eye movement features (the basic statistical, pupillary response, fixation density, fixation semantic and saccade encoding features). We adopted a random forest and performed biometric recognition for both identification and verification scenarios. The best accuracy we obtained in the identification scenario was 78% (equal error rate, EER = 6.3%) with the 10-fold classification and 64% (EER = 12.1%) with the leave-one-route-out classification. The best accuracy we achieved in the verification scenario was 89% (EER = 9.1%). Additionally, we tested performance across the 5 feature sets and 20 time window sizes. The results showed that the verification accuracy was insensitive to the increase in the time window size. These findings are the first indication of the viability of performing implicit and stimulus-independent biometric recognition in real-world settings using wearable eye tracking.