Utilizing AlexNet Deep Transfer Learning for Ear Recognition

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.