Abstract:
Biometric recognition systems are an integral part of security in modern computer systems. Contrary to related work, our approach to biometrics does not base on individua...Show MoreMetadata
Abstract:
Biometric recognition systems are an integral part of security in modern computer systems. Contrary to related work, our approach to biometrics does not base on individual patterns in sensor data. We measure visual self-recognition as an in-brain identity validation mechanism and aim to employ it as a biometric trait. This work builds on a public data set of an eye-tracking study investigating the visual self-recognition of 116 volunteers. Exploration of this high-quality data revealed significant effects of self-recognition in the time course of pupil size and microsaccade generation. These findings facilitated the extraction of features to train four different experimental classification models. The best-performing model is a pre-trained classifier on integrated data by achieving an accuracy of 0.82. Our research supports the hypothesis that self-recognition can be a feasible biometric trait. Furthermore, we found that self-recognition can be measured using eye-tracking devices. Future work on model optimization and additional feature extraction is required. It will enable to introduce technologies utilizing self-recognition to real-world applications.
Date of Conference: 25-28 September 2023
Date Added to IEEE Xplore: 01 March 2024
ISBN Information: