Domain adaptation for unconstrained ear recognition with convolutional neural networks.
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Date
2022
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Abstract
Automatic recognition using ear images is an active area of interest within the biometrics
community. Human ears are a stable and reliable source of information since they are not
affected by facial expressions, do not suffer extreme change over time, are less prone to
injuries, and are fully visible in mask-wearing scenarios. In addition, ear images can be
passively captured from a distance, making it convenient when implementing surveillance
and security applications. At the same time, deep learning-based methods have proven
to be powerful techniques for unconstrained recognition. However, to truly benefit from
deep learning techniques, it is necessary to count on a large-size variable set of samples to
train and test networks. In this work, we built a new dataset using the VGGFace dataset,
fine-tuned pre-train deep models, analyzed their sensitivity to different covariates in data,
and explored the score-level fusion technique to improve overall recognition performance.
Open-set and close-set experiments were performed using the proposed dataset and the
challenging UERC dataset. Results show a significant improvement of around 9% when
using a pre-trained face model over a general image recognition model; in addition, we
achieve 4% better performance when fusing scores from both models.
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Keywords
Transfer learning
Citation
RAMOS COOPER, S.; CÁMARA CHÁVEZ, G. Domain adaptation for unconstrained ear recognition with convolutional neural networks. CLEI electronic journal, v. 25, n. 2, artigo 8, maio 2022. Disponível em: <https://www.clei.org/cleiej/index.php/cleiej/article/view/532>. Acesso em: 06 jul. 2023.