Earprint recognition using deep learning technique

Hamdany, Arwa Hamed Salih; Ebrahem, Aseel Thamar; Alkababji, Ahmed M.

doi:10.12928/telkomnika.v19i2.16572

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…These results refer that proposed technique outperform the compared methods, as well as it utilized techniques such as DWT & PSO to improve the efficiency of the proposed work. Table 6: the results of the comparison between the proposed method and some recent method Method R.R LBP/Laplacian filter method [36] 80% Deep earprint learning [37] 94% The method in [38] 97.36% Proposed method 99%…”

Section: Comparing the Proposed Methods With Some Recent Methodsmentioning

confidence: 99%

An Intelligent Ear Recognition Technique

Hussein¹,

Sahan²

2021

IJASCA

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The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.

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Section: Comparing the Proposed Methods With Some Recent Methodsmentioning

confidence: 99%

An Intelligent Ear Recognition Technique

Hussein¹,

Sahan²

2021

IJASCA

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“…The performance of this network is tested on AMI and IIT Delhi II datasets, where effect of varying the network parameters (e.g., learning rate and activation function) was studied. Similarly, Hamdany et al ( 2021 ) proposed a CNN architecture for ear recognition. Different back-propagation techniques, including Adaptive moment estimation (ADAM) and stochastic gradient descent with momentum (SGDM), were tested.…”

Section: Related Workmentioning

confidence: 99%

MDFNet: an unsupervised lightweight network for ear print recognition

Aiadi

Khaldi

Saadeddine

2022

J Ambient Intell Human Comput

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In this paper, we propose an unsupervised lightweight network with a single layer for ear print recognition. We refer to this method by MDFNet because it relies on gradient Magnitude and Direction alongside with responses of data-driven Filters. At first, we align ear using Convolution Neural Network (CNN) and Principal Component Analysis (PCA). MDFNet starts by generating a filter bank from training images using PCA. This is followed by a twofold layer, which comprises two operations namely convolution using learned filters and computation of gradient image. To prevent over-fitting, a binary hashing process is done by combining different filter responses into a single feature map. Then, we separately construct histograms for each of gradient magnitude and direction according to the feature map. These histograms are then normalized, using power-L 2 rule, to cope with illumination disparity. Several fusion rules are evaluated to combine the two histograms. The main novelty of MDFNet lies in its simple architecture, effectiveness, the good compromise between processing time and performance it provides along with its high robustness to occlusion. We conduct extensive experiments on three public datasets namely AWE, AMI and IIT Delhi II. Experimental results demonstrate the effectiveness of MDFNet, which achieves high recognition rates (82.5%, 97.67% and 98.96%, respectively), and outperformed several state of the art methods with a high robustness to occlusion. Experiments revealed also the actual need for considering ear alignment.

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