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How to Beautify the Elderly?: A Study on the Facial Preference of Senior Citizens

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Human Aspects of IT for the Aged Population. Technology and Society (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12209))

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Abstract

Selfie is gaining popularity among the senior citizens, but for them using the “beauty” function is not very friendly. In this research, we intended to figure out the facial feature about what senior people care most and to build a prototype for the beautification application. This paper presents two studies: an online survey about senior citizens’ behavior on taking photos and a qualitative study on senior citizens’ aesthetics preference of faces. By analyzing the data we collected from the studies above, we find how the elderly think and behave are quite different from the traditional assumption. A simple beautification system is built based on the style GAN (Generative Adversarial Network) algorithm according to our conclusions. These results may provide a reference for future designs for beautification products.

Supported by organization the Future Lab at Tsinghua University.

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References

  1. Oxford Dictionaries (2013). https://en.oxforddictionaries.com/definition/selfie. Selfie

  2. Katz, J.E., Crocker, E.T.: Selfies and photo messaging as visual conversation: reports from the United States, United Kingdom, and China. Int. J. Commun. 9(1), 1861–1872 (2015)

    Google Scholar 

  3. Chae, J.: Virtual makeover: selfie-taking and social media use increase selfie-editing frequency through social comparison. Comput. Hum. Behav. 66, 370–376 (2017)

    Article  Google Scholar 

  4. Kim, E., Lee, J.A., Sung, Y., et al.: Predicting selfie-posting behavior on social networking sites: an extension of theory of planned behavior. Comput. Hum. Behav. 62, 116–123 (2016)

    Article  Google Scholar 

  5. Dhir, A., Kaur, P., Lonka, K., Nieminen, M.: Why do adolescents untag photos on Facebook? Comput. Hum. Behav. 55, 1106–1115 (2016)

    Article  Google Scholar 

  6. Pew Internet: Social media update 2013 (2013). http://www.pewinternet.org/2013/12/30/social-media-update-2013/

  7. Qiu, L., Lu, J., Yang, S., et al.: What does your selfie say about you? Comput. Hum. Behav. 52, 443–449 (2015)

    Article  Google Scholar 

  8. Hartmann, J., Heitmann, M., Schamp, C., et al.: The Power of Brand Selfies in Consumer-Generated Brand Images. Social Science Electronic Publishing (2019)

    Google Scholar 

  9. Cao, Y., O’Halloran, K.: Learning human photo shooting patterns from large-scale community photo collections. Multimedia Tools Appl. 74(24), 11499–11516 (2015)

    Article  Google Scholar 

  10. Dhir, A., Pallesen, S., Torsheim, T., et al.: Do age and gender differences exist in selfie-related behaviours? Comput. Hum. Behav. 63, 549–555 (2016)

    Article  Google Scholar 

  11. Manago, A.M., Graham, M.B., Greenfield, P.M., et al.: Self-presentation and gender on MySpace. J. Appl. Dev. Psychol. 29(6), 446–458 (2008)

    Article  Google Scholar 

  12. Judite, G., Isabel, G.M., Miguel, F., et al.: Selfie aging index: an index for the self-assessment of healthy and active aging. Front. Med. 4, 236 (2017)

    Article  Google Scholar 

  13. Suo, J.L., Min, F., Zhu, S.C., et al.: A multi-resolution dynamic model for face aging simulation. In: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 18–23 June 2007, Minneapolis, Minnesota, USA. IEEE (2007)

    Google Scholar 

  14. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, no. 2 (2012)

    Google Scholar 

  15. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2013). https://doi.org/10.1109/CVPR.2014.81

  16. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks, pp. 1–10 (2016)

    Google Scholar 

  17. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger, pp. 6517-6525 (2017). https://doi.org/10.1109/CVPR.2017.690

  18. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)

    Google Scholar 

  19. Goodfellow, I.J., et al.: Generative adversarial networks. In: Advances in Neural Information Processing Systems (2014)

    Google Scholar 

  20. Karras, T., Aila, T., Laine, S., et al.: Progressive growing of GANs for improved quality, stability, and variation (2017)

    Google Scholar 

  21. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks, pp. 4396–4405 (2019). https://doi.org/10.1109/CVPR.2019.00453

  22. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN (2019)

    Google Scholar 

  23. Lipton, Z.C., Tripathi, S.: Precise recovery of latent vectors from generative adversarial networks (2017)

    Google Scholar 

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Acknowledge

This work is supported by NO. 20197010002, Tsinghua University Research Funding. We would like to thank professor Jihong Jeung, Jiabei Jiang and Yuhao Huang for their help and support.

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Correspondence to Jihong Jeung .

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Zhang, W., Li, Y., Jeung, J. (2020). How to Beautify the Elderly?: A Study on the Facial Preference of Senior Citizens. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. Technology and Society. HCII 2020. Lecture Notes in Computer Science(), vol 12209. Springer, Cham. https://doi.org/10.1007/978-3-030-50232-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-50232-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50231-7

  • Online ISBN: 978-3-030-50232-4

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