An Improved Conditional Generative Adversarial Network for Translating Depth Image from Color Image and Accurate Hand Gesture Recognition

Shurong Chai, Jiaqing Liu, Tomoko Tateyama, Yutaro Iwamoto, Yen Wei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pix2Pix is a common method in the image-to-image translation task. In the field of gesture recognition, previous studies employed Pix2Pix for translating color images to depth images to improve the accuracy of the original color images. However, they mainly focused on improving the quality of the generated images, ignoring the goal of classifying gestures. In this study, we propose a discriminative Pix2Pix for translating depth images from color images. Our motivation is to generate more understandable images for neural networks instead of those for humans. We introduce a new discriminator called Feature-level Discriminator (FLD). The original Pix2Pix discriminator can be considered a Image-level Discriminator (ILD). FLD distinguishes the extracted feature map of an image by a specified convolutional neural network (CNN), whereas ILD focuses more on images. We evaluate our approach on the OUHAND dataset, indicating that FLD can significantly improve the accuracy of the generated image and color image using a two-stream framework.

Original languageEnglish
Title of host publication2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages789-792
Number of pages4
ISBN (Electronic)9781665436762
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
Duration: 12-10-202115-10-2021

Publication series

Name2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/TerritoryJapan
CityKyoto
Period12-10-2115-10-21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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