Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/10612
Title: Utilizing AlexNet Deep Transfer Learning for Ear Recognition
Authors: Almisreb, A.A. 
Jamil, N. 
Din, N.M. 
Issue Date: 2018
Abstract: Transfer Learning is an efficient approach of solving classification problem with little amount of data. In this paper, we applied Transfer Learning to the well-known AlexNet Convolution Neural Network (AlexNet CNN) for human recognition based on ear images. We adopted and fine-tuned AlexNet CNN to suit our problem domain. The last fully connected layer is replaced with another fully connected layer to recognize 10 classes instead of 1000 classes. Another Rectified Linear Unit (ReLU) layer is also added to improve the non-linear problem-solving ability of the network. To train the fine-tuned network, we allocate 250 ear images taken from 10 subjects for training, and 50 ear images are used for validation and testing. The proposed fine-tuned network works well in our application as we get 100% validation accuracy. © 2018 IEEE.
DOI: 10.1109/INFRKM.2018.8464769
Appears in Collections:CSIT Scholarly Publication

Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.