A brief review of the ear recognition process using deep neural networks

Galdámez, Pedro Luis; Raveane, William; Arrieta, Angélica González

doi:10.1016/j.jal.2016.11.014

Cited by 49 publications

(32 citation statements)

References 13 publications

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“…Since then, many researchers have been interested in exploring this emerging biometric modality and finding robust ways to represent ear images and extract their distinguishable features for constructing personal identification systems. For chronological developments of ear recognition techniques, several surveys and reviews exist, summarizing the achievements, limitations, and challenges encountered [3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…The model was tested under occlusion and rotation and showed a satisfactory recognition rate when the degree of distortions was small. The authors in [6] proposed a standard CNN-based ear recognition system and several models were trained using ear images acquired under controlled conditions of lighting, quality, size, and viewing angles. Their system obtained good results when recognizing ear images similar to what the model was trained on.…”

Section: Introductionmentioning

confidence: 99%

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Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition

Alshazly

Linse

Barth

et al. 2019

Sensors

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The recognition performance of visual recognition systems is highly dependent on extracting and representing the discriminative characteristics of image data. Convolutional neural networks (CNNs) have shown unprecedented success in a variety of visual recognition tasks due to their capability to provide in-depth representations exploiting visual image features of appearance, color, and texture. This paper presents a novel system for ear recognition based on ensembles of deep CNN-based models and more specifically the Visual Geometry Group (VGG)-like network architectures for extracting discriminative deep features from ear images. We began by training different networks of increasing depth on ear images with random weight initialization. Then, we examined pretrained models as feature extractors as well as fine-tuning them on ear images. After that, we built ensembles of the best models to further improve the recognition performance. We evaluated the proposed ensembles through identification experiments using ear images acquired under controlled and uncontrolled conditions from mathematical analysis of images (AMI), AMI cropped (AMIC) (introduced here), and West Pomeranian University of Technology (WPUT) ear datasets. The experimental results indicate that our ensembles of models yield the best performance with significant improvements over the recently published results. Moreover, we provide visual explanations of the learned features by highlighting the relevant image regions utilized by the models for making decisions or predictions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition

Alshazly

Linse

Barth

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The first auto-encoder consists of input layer and the first hidden layer, and so on. The training of DNN contains two sections: pre-training and fine-tuning [ 57 , 58 , 59 ]. During the process of the first section based on unsupervised learning, the encoder vector of x(i) obtained from the first auto-encoder is:

where

represents the parameter of the first auto-encoder.…”

Section: Proposed Diagnostic Modelmentioning

confidence: 99%

A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

Shaohu

Liu

2020

Sensors

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Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.

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“…Their work secured an accuracy level of 95% predictability. The paper of [30], although not related to biometry, presents the use of transfer learning on different CNN architectures for breast cancer detection. In their paper, GoogleNet, VGGNet, and ResNet are used and secured promising results.…”

Section: Review Of Related Studies and Literaturesmentioning

confidence: 99%

Unconstrained Ear Recognition through Domain Adaptive Deep Learning Models of Convolutional Neural Network

Alejo¹,

Hate²

2019

IJRTE

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Limited ear dataset yields to the adaption of domain adaptive deep learning or transfer learning in the development of ear biometric recognition. Ear recognition is a variation of biometrics that is becoming popular in various areas of research due to the advantages of ears towards human identity recognition. In this paper, handpicked CNN architectures: AlexNet, GoogLeNet, Inception-v3, Inception-ResNet-v2, ResNet-18, ResNet-50, SqueezeNet, ShuffleNet, and MobileNet-v2 are explored and compared for use in an unconstrained ear biometric recognition. 250 unconstrained ear images are collected and acquired from the web through web crawlers and are preprocessed with basic image processing methods including the use of contrast limited adaptive histogram equalization for ear image quality improvement. Each CNN architecture is analyzed structurally and are fine-tuned to satisfy the requirements of ear recognition. Earlier layers of CNN architectures are used as feature extractors. Last 2-3 layers of each CNN architectures are fine-tuned thus, are replaced with layers of the same kind for ear recognition models to classify 10 classes of ears instead of 1000. 80 percent of acquired unconstrained ear images is used for training and the remaining 20 percent is reserved for testing and validation. Results of each architectures are compared in terms of their training time, training and validation outputs as such learned features and losses, and test results in terms of above-95% accuracy confidence. Above all the used architectures, ResNet, AlexNet, and GoogleNet achieved an accuracy confidence of 97-100% and is best for use in unconstrained ear biometric recognition while ShuffleNet, despite of achieving approximately 90%, shows promising result for use in mobile version of unconstrained ear biometric recognition.

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A brief review of the ear recognition process using deep neural networks

Cited by 49 publications

References 13 publications

Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition

Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition

A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

Unconstrained Ear Recognition through Domain Adaptive Deep Learning Models of Convolutional Neural Network

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