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Kim, Jang Su
- Neighborhood Loss for Age Estimation from Face Image Using Convolutional Neural Networks
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Authors
Affiliations
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, KP
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, KP
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2770-2774Abstract
Convolutional Neural Network (CNN) is widely used in estimating age from face image. In many CNN applications such as image classification, face recognition and other computer vision scopes, the cross-entropy loss is used as a supervision signal to train CNN model. However, the cross-entropy loss only enhances the separability of classes and does not consider their correlation in age estimation task. In this paper we propose a novel loss function called neighborhood loss which regards the correlation between classes in age estimation by modifying standard cross entropy loss. To evaluate the effectiveness of the proposed neighborhood loss, we present CNN architecture based on the residual units. Through some experiments, we show that neighborhood loss provides superior performance compared to prior works in age estimation.Keywords
Age Estimation, Neighborhood Loss, Convolutional Neural Network.
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Abstract Views :278 |
PDF Views:0
Authors
Affiliations
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, DPR of Korea., KP
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, DPR of Korea., KP
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2899-2903Abstract
Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of computer vision tasks in a great extent. Existing low-light image restoration methods still have limitation in image naturalness and noise. In this paper, we propose an efficient deep residual network that learns difference map between low-light image and original image and restores the low-light image. Additionally, we propose a new low-light image generator, which is used to train the deep residual network. Especially the proposed generator can simulate low-light images containing luminance sources and completely darkness parts. Our experiments demonstrate that the proposed method achieves good results for both synthetic and natural low-light images.Keywords
Low-Light Image, Image Restoration, Deep Residual Network.References
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