Mobile ear recognition application

Oravec, Milos; Pavlovičová, Jarmila; Sopiak, Dominik; Jirka, Vojtech; Lóderer, Marek; Lehota, Lubos; Vodicka, Marek; Fackovec, Matej; Mihalik, Matej; Tomik, Martin; Gerat, Jozef

doi:10.1109/iwssip.2016.7502719

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…In addition, there are several desirable characteristics of the human ears which include: ease of capture from a distance, stability over time, ability to identify identical twins [1], and being insensitive to emotions and facial expressions [2], [3]. Given these appealing features we can build and develop reliable recognition systems on numerous devices in a non-intrusive and non-distracting manner [4], [5], [6]. Nevertheless, an accurate recognition can be a challenging task when ear images are acquired in unconstrained environments where various appearance variations and illumination changes need to be considered [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

et al. 2020

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This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear recognition on the unconstrained EarVN1.0 dataset. As the dataset size is still insufficient to train deep CNNs from scratch, we utilize transfer learning and propose different domain adaptation strategies. The experiments show that our networks, which are fine-tuned using custom-sized inputs determined specifically for each CNN architecture, obtain state-of-the-art recognition performance where a single ResNeXt101 model achieves a rank-1 recognition accuracy of 93.45%. Moreover, we achieve the best rank-1 recognition accuracy of 95.85% using an ensemble of fine-tuned ResNeXt101 models. In order to explain the performance differences between models and make our results more interpretable, we employ the t-SNE algorithm to explore and visualize the learned features. Feature visualizations show well-separated clusters representing ear images of the different subjects. This indicates that discriminative and ear-specific features are learned when applying our proposed learning strategies.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

et al. 2020

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“…Earprint recognition using deep learning technique (Arwa H. Salih Hamdany) 433 have been examined for the DEL network such as stochastic gradient descent with momentum (SGDM), and root mean square propagation (RMSProp) [7,8]. The remaining sections are distributed as follows: section 2 provides the literature review of this paper, section 3 describes the DEL method, section 4 discusses the results and section 5 declares the conclusion.…”

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confidence: 99%

Earprint recognition using deep learning technique

2021

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Earprint has interestingly been considered for recognition systems. It refers to the shape of ear, where each person has a unique shape of earprint. It is a strong biometric pattern and it can effectively be used for authentications. In this paper, an efficient deep learning (DL) model for earprint recognition is designed. This model is named the deep earprint learning (DEL). It is a deep network that carefully designed for segmented and normalized ear patterns. IIT Delhi ear database (IITDED) version 1.0 has been exploited in this study. The best obtaining accuracy of 94% is recorded for the proposed DEL.

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An Experimental Study Using Scale Invariant Feature Transform and Key-Point Extraction for Human Ear Recognition System

Som

Mahajan

2019

Advances in Intelligent Systems and Computing

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Mobile ear recognition application

Cited by 9 publications

References 4 publications

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

Earprint recognition using deep learning technique

An Experimental Study Using Scale Invariant Feature Transform and Key-Point Extraction for Human Ear Recognition System

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