Age-constrained Ear Recognition: The EICZA Dataset and SASE Baseline Model

Using the ear as a biometric identifier, particularly for children in healthcare settings, has an important advantage over using the face - the privacy of the person can be protected better. However, aging and the resulting appearance differences, known to be challenges for face recognition models, have not been addressed for ear recognition yet. To address this limitation, we curated a publicly available dataset, which we call Ears of Infant Cohort in Zambia with Aging (EICZA)
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. The dataset contains 3,330 ear images of 177 subjects, each photographed multiple times between the ages of 6 days and 9 months, when ear growth is most significant. For the task of age-constrained ear recognition, i.e., recognizing a person who has aged since the model was trained, we propose a new ear recognition model, called SASE for Self-Attention-based Sequential Ear image analysis. The model takes a sequence of ear images at early but different ages as input (instead of a single image) and processes them with a feature extraction network and a Transformer encoder. Trained with a large margin cosine loss function, the model is encouraged to learn a feature representation that distinguishes subjects from each other: Our experiments show that accounting for age enables our model to outperform other models that do not in recognizing ears that have grown and look different in later time periods.