VGGFace-Ear: An Extended Dataset for Unconstrained Ear Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 5; p. 1752
• Main Authors: Ramos-Cooper, Solange, Gomez-Nieto, Erick, Camara-Chavez, Guillermo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.02.2022; MDPI
• Subjects: Annotations; Biometrics; Biometry; convolutional neural networks; Datasets; Deep learning; Digital cameras; Ear; ear biometrics; ear recognition; Facial Recognition; Humans; mask-RCNN; Neural networks; Neural Networks, Computer; Recognition, Psychology; VGGFace; deep learning; transfer learning; ear biometrics; VGGFace; ear recognition; mask-RCNN; convolutional neural networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22051752

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.