VGGFace-Ear : an extended dataset for unconstrained ear recognition.
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Date
2022
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Abstract
Recognition using ear images has been an active field of research in recent years. Besides faces
and fingerprints, ears have a unique structure to identify people and can be captured from a distance,
contactless, and without the subject’s cooperation. Therefore, it represents an appealing choice
for building surveillance, forensic, and security applications. However, many techniques used
in those applications—e.g., convolutional neural networks (CNN)—usually demand large-scale
datasets for training. This research work introduces a new dataset of ear images taken under
uncontrolled conditions that present high inter-class and intra-class variability. We built this dataset
using an existing face dataset called the VGGFace, which gathers more than 3.3 million images.
in addition, we perform ear recognition using transfer learning with CNN pretrained on image and
face recognition. Finally, we performed two experiments on two unconstrained datasets and reported
our results using Rank-based metrics.
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Keywords
Ear biometrics, Deep learning, Convolutional neural networks, Transfer learning
Citation
RAMOS COOPER, S.; GOMEZ NIETO, E. M.; CÁMARA CHÁVEZ, G. VGGFace-Ear: an extended dataset for unconstrained ear recognition. Sensors, v. 22, n. 5, artigo 1752, 2022. Disponível em: <https://www.mdpi.com/1424-8220/22/5/1752>. Acesso em: 06 jul. 2022.