TY - GEN
T1 - An Improved Conditional Generative Adversarial Network for Translating Depth Image from Color Image and Accurate Hand Gesture Recognition
AU - Chai, Shurong
AU - Liu, Jiaqing
AU - Tateyama, Tomoko
AU - Iwamoto, Yutaro
AU - Chen, Yen Wei
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85123470589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123470589&partnerID=8YFLogxK
U2 - 10.1109/GCCE53005.2021.9621801
DO - 10.1109/GCCE53005.2021.9621801
M3 - Conference contribution
AN - SCOPUS:85123470589
T3 - 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
SP - 789
EP - 792
BT - 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Y2 - 12 October 2021 through 15 October 2021
ER -