Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification

View Sample PDF

Author(s): Sahil Sharma (Thapar Institute of Engineering and Technology, India)and Vijay Kumar (Thapar Institute of Engineering and Technology, India)
Copyright: 2019
Pages: 17
Source title: Handbook of Research on Deep Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt), Ashraf Darwish (Helwan University, Egypt)and Chiranji Lal Chowdhary (VIT University, India)
DOI: 10.4018/978-1-5225-7862-8.ch006

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

Purchase

View Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification on the publisher's website for pricing and purchasing information.

Abstract

Face depth image has been used for occlusion presence and gender prediction by transfer learning. This chapter discusses about the overfitting problem and how augmentation helps overcoming it. Pre-processing of the dataset includes converting a 3D object image into depth image for further processing. Five state-of-the-art 2D deep learning models (e.g., AlexNet, VGG16, DenseNet121, ResNet18, and SqueezeNet) have been discussed along with their architecture. The effect of increasing the number of epochs on the top-1 error rate has been presented in the experimental section. The result section consists of error rates in comparison of with and without augmentation on the datasets.

The IRMA Community

Research IRM

Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification

Purchase

Abstract

Related Content

IRMA Sponsors