Unconventional Biometrics

This thesis is a paper collection that focuses on unconventional methods of biometric recognition. Four new approaches are presented and discussed. The first two introduce and explore the concepts behind transient biometrics. Transient biometrics relaxes the hard permanence requirement that is common to biometric identifiers, creating a biometric signature with expiration date which increases acceptability. The third approach investigates a novel method for extracting a capable biometric identifier using Electroencephalography (EEG) and a visual stimulus. The final approach studies the use of synthetic biometric data for training a machine learning approach in the recognition of non-collaborative subjects under the context or person re-identification. Four new datasets have been created for the purposes of this thesis and have been made publicly available. Contributions are on the interface between computer vision, biometrics and machine learning. Ethical implications of this work are discussed, concluding that it is preferable to perform such work in the public domain.