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Title: VGGFace-Ear : an extended dataset for unconstrained ear recognition.
Authors: Ramos Cooper, Solange
Gómez Nieto, Erick Mauricio
Cámara Chávez, Guillermo
Keywords: Ear biometrics
Deep learning
Convolutional neural networks
Transfer learning
Issue Date: 2022
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: <>. Acesso em: 06 jul. 2022.
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.
ISSN: 1424-8220
metadata.dc.rights.license: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/). Fonte: o PDF do artigo.
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