Zero-shot ear cross-dataset transfer for person recognition on mobile devices

Smartphones contain personal and private data to be protected, such as everyday communications or bank accounts. Several biometric techniques have been developed to unlock smartphones, among which ear biometrics represents a natural and promising opportunity even though the ear can be used in other biometric and multi-biometric applications. A problem in generalizing research results to real-world applications is that the available ear datasets present different characteristics and some bias. This paper stems from a study about the effect of mixing multiple datasets during the training of an ear recognition system. The main contribution is the evaluation of a robust pipeline that learns to combine data from different sources and highlights the importance of pre-training encoders on auxiliary tasks. The reported experiments exploit eight diverse training datasets to demonstrate the generalization capabilities of the proposed approach. Performance evaluation includes testing with collections not seen during training and assessing zero-shot cross-dataset transfer. The results confirm that mixing different sources provides an insightful perspective on the datasets and competitive results with some existing benchmarks.