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A study of age estimation from occluded images

가림이 있는 얼굴 영상의 나이 인식 연구

  • 최성은 (한양여자대학교 빅데이터과)
  • Received : 2022.08.26
  • Accepted : 2022.09.27
  • Published : 2022.09.30
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Abstract

Research on facial age estimation is being actively conducted because it is used in various application fields. Facial images taken in various environments often have occlusions, and there is a problem in that performance of age estimation is degraded. Therefore, we propose age estimation method by creating an occluded part using image extrapolation technology to improve the age estimation performance of an occluded face image. In order to confirm the effect of occlusion in the image on the age estimation performance, an image with occlusion is generated using a mask image. The occluded part of facial image is restored using SpiralNet, which is one of the image extrapolation techniques, and it is a method to create an occluded part while crossing the edge of an image. Experimental results show that age estimation performance of occluded facial image is significantly degraded. It was confirmed that the age estimation performance is improved when using a face image with reconstructed occlusions using SpiralNet by experiments.

얼굴 영상에서 나이를 인식하는 기술은 여러 응용분야에서 활용되면서 그에 대한 연구가 활발히 진행되고 있다. 다양한 환경에서 촬영된 얼굴 영상은 얼굴의 일부가 가려지는 경우가 많으며 이는 나이 인식 성능에 영향을 미치게 된다. 따라서 본 논문에서는 가림이 있는 얼굴 영상의 나이 인식 성능을 개선하기 위해, Image Extrapolation 기술을 이용하여 가려진 부분을 생성하여 나이를 인식하는 방법을 제안한다. 영상에서의 가림이 나이 인식 성능에 미치는 영향을 확인하기 위해서 마스크 이미지를 적용하여 가림이 있는 얼굴 영상을 생성하였다. 가림에 의해 나이 인식 성능이 저하되는 문제를 해결하기 위해, Image Extrapolation 기술 중 영상의 가장자리를 순회하면서 가려진 부분을 생성하는 SpiralNet 을 사용하여 가려진 부분을 예측하여 생성하고 얼굴 나이 인식에 사용하였다. 실험을 통해 가림이 있는 영상에서 나이 인식 성능이 저하되는 문제가 있고, SpiralNet으로 가림 부분을 생성한 영상으로 나이를 인식하면 나이 인식 성능이 개선되는 것을 확인하였다.

Keywords

Acknowledgement

본 논문은 2022년도 1기 한양여자대학교 교내 연구비에 의하여 연구됨

References

  1. Raphael Angulu1, Jules R. Tapamo and Aderemi O. Adewumi, "Age estimation via face images: a survey", EURASIP Journal on Image and Video Processing 2018
  2. Guo, Dongsheng, Liu, Hongzhi, Zhao, Haoru, Cheng, Yunhao, Song, Qingwei, Gu, Zhaorui, Zheng, Haiyong and Zheng, Bing, "Spiral Generative Network for Image Extrapolation", The European Conference on Computer Vision (ECCV), p.701-717, 2020
  3. Rothe, R., Timofte, R. Van Gool, L., Deep Expectation of Real and Apparent Age from a Single Image without Facial Landmarks. International Journal of Computer Vision, 2018, 126, 144-157 https://doi.org/10.1007/s11263-016-0940-3
  4. Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo Wang, Alan Yuille, Deep Regression Forests for Age Estimation, IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
  5. Chao Zhang, Shuaicheng Liu, Xun Xu, Ce Zhu, C3AE: Exploring the Limits of Compact Model for Age Estimation, CVPR 2019
  6. Xinhua Liu, Yao Zou, Hailan Kuang and Xiaolin Ma, Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network, MDPI, 2020
  7. Hongyu Pan, Hu Han, Shiguang Shan, Xilin Chen, Mean-Variance Loss for Deep Age Estimation from a Face, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  8. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS. pp. 2672-2680 (2014)
  9. Wang, Y., Tao, X., Shen, X., Jia, J.: Wide-context semantic image extrapolation. In: CVPR. pp. 1399-1408 (2019)
  10. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV. pp. 1501-1510 (2017)
  11. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: CVPR. pp. 2337-2346 (2019)
  12. Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen, "Progressive Growing of GANs for Improved Quality, Stability, and Variation", ICLR 2018 paper
  13. The FG-NET Aging Database, Available: http://sting.cycollege.ac.cy/~alanitis/fgnetaging/index.htm, 2009
  14. UTKFace, http://aicip.eecs.utk.edu/wiki/UTKFace
  15. Rasmus Rothe, Radu Timofte, Luc Van Gool, DEX: Deep EXpectation of apparent age from a single image, Looking at People Workshop, International Conference on Computer Vision (ICCV), 2015
  16. Adrian Bulat, Georgios Tzimiropoulos, "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)," International conference on computer vision, 2017.
  17. Karen Simonyan, Andrew Zisserman, Very Deep Convolutional Networks for large-scale image recognition, ICLR 2015
  18. Alex Krizhevsky,Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS 2012