NDLI: Domain adaptation for ear recognition using deep convolutional neural networks

Content Provider	IET Digital Library
Author	Eyiokur, Fevziye Irem Yaman, Dogucan Ekenel, Hazım Kemal
Abstract	Here, the authors have extensively investigated the unconstrained ear recognition problem. The authors have first shown the importance of domain adaptation, when deep convolutional neural network (CNN) models are used for ear recognition. To enable domain adaptation, the authors have collected a new ear data set using the Multi-PIE face data set, which they named as Multi-PIE ear data set. The authors have analysed in depth the effect of ear image quality, for example, illumination and aspect ratio, on the classification performance. Finally, the authors have addressed the problem of data set bias in the ear recognition field. Experiments on the UERC data set have shown that domain adaptation leads to a significant performance improvement. For example, when VGG-16 model is used and the domain adaptation is applied, an absolute increase of around 10% has been achieved. Combining different deep CNN models has further improved the accuracy by 4%. In the experiments that the authors have conducted to examine the data set bias, given an ear image, they were able to classify the data set that it has come from with 99.71% accuracy, which indicates a strong bias among the ear recognition data sets.
Starting Page	199
Ending Page	206
Page Count	8
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 3, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0209
Journal	IET Biometrics
Publisher Date	2017-12-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classification Performance Computer Vision And Image Processing Technique Deep CNN Model Deep Convolutional Neural Network Domain Adaptation Ear Image Quality Ear Recognition Image Classification Image Recognition MultiPIE Face Dataset Neural Computing Technique Neural Nets Object Recognition VGG-16 Model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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