Facial Recognition of Emotions with Smartphones to Improve the Elder Quality of Life

  • Sheila BonillaEmail author
  • Enrique Moguel
  • Jose Garcia-Alonso
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1016)


The increase in the elderly population today is a fact. This group of people needs day-to-day care due to their age and, in addition, they often have health problems. Technology can be used to mitigate these problem. However, it must be beared in mind that most of this population is currently unable to get the most out of electronic devices. To help elders benefit from these devices systems adapted to their needs and preferences are needed. In particular, systems that use the elders contextual information to integrate several aspects of eldercare and adapt them to each elder would provide significant benefits. In this paper, we propose to use smartphones as the device who centralizes contextual information of the elders, focusing on emotion recognition. These emotions will be used to recognize to what extent an elderly person needs care at certain times of the day and to adapt surrounding IoT systems to their needs and moods.


IoT Facial recognition of emotion Smartphone Elderly 



This work was supported by 4IE project (0045-4IE-4-P) funded by the Interreg V-A España-Portugal (POCTEP) 2014–2020 program, by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2014-53986-REDT and TIN2015-69957-R (MINECO/FEDER)), by the Department of Economy and Infrastructure of the Government of Extremadura (GR15098), and by the European Regional Development Fund.


  1. 1.
    Agarwal, S., Santra, B., Mukherjee, D.P.: Anubhav: recognizing emotions through facial expression. Vis. Comput. 34(2), 177–191 (2018)CrossRefGoogle Scholar
  2. 2.
    Berrocal, J., Garcia-Alonso, J., Murillo, J.M., Canal, C.: Rich contextual information for monitoring the elderly in an early stage of cognitive impairment. Pervasive Mob. Comput. 34, 106–125 (2017)CrossRefGoogle Scholar
  3. 3.
    Dantcheva, A., Bilinski, P., Broutart, J.C., Robert, P., Bremond, F.: Emotion facial recognition by the means of automatic video analysis. Gerontechnol. J. (2016)Google Scholar
  4. 4.
    Flores-Martin, D., Pérez-Vereda, A., Berrocal, J., Canal, C., Murillo, J.M.: Coordinación de Dispositivos IoT mediante Web Semántica y Ontologías en Situational-Context. JISBD (2018)Google Scholar
  5. 5.
    Garcia-Alonso, J., Berrocal, J., Murillo, J.M., Mendes, D., Fonseca, C., Lopes, M.: Situational-context for virtually modeling the elderly. In: Novais, P., et al. (eds.) ISAmI 2018. AISC, vol. 806, pp. 298–305. Springer, Cham (2019). Scholar
  6. 6.
    Goyal, S.J., Upadhyay, A.K., Jadon, R.S., Goyal, R.: Real-life facial expression recognition systems: a review. In: Satapathy, S.C., Bhateja, V., Das, S. (eds.) Smart Computing and Informatics. SIST, vol. 77, pp. 311–331. Springer, Singapore (2018). Scholar
  7. 7.
    Hossain, M.S., Muhammad, G.: Cloud-assisted speech and face recognition framework for health monitoring. Mob. Netw. Appl. 20(3), 391–399 (2015)CrossRefGoogle Scholar
  8. 8.
    Kulkarni, A., Shendge, A., Varma, V., Kimmatkar, N.V.: Intelligent emotion detection system using facial images (2018)Google Scholar
  9. 9.
    Li, H., Buenaposada, J.M., Baumela, L.: Real-time facial expression recognition with illumination-corrected image sequences. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2008, pp. 1–6. IEEE (2008)Google Scholar
  10. 10.
    Lozano-Monasor, E., López, M.T., Vigo-Bustos, F., Fernández-Caballero, A.: Facial expression recognition in ageing adults: from lab to ambient assisted living. J. Ambient Intell. Humanized Comput. 8(4), 567–578 (2017)CrossRefGoogle Scholar
  11. 11.
    Moguel, E., et al.: Enriched elderly virtual profiles by means of a multidimensionalidad integrated assessment platform (2018)Google Scholar
  12. 12.
    Núñez, C.A.V., Mendoza, P.S., Hernández, K.A., Molinares, D.J.: Internet de las cosas y la salud centrada en el hogar. Salud Uninorte 32(2) (2016)Google Scholar
  13. 13.
    Rodrigues, R., Huber, M., Lamura, G., et al.: Facts and figures on healthy ageing and long-term care. European Centre for Social Welfare Policy and Research, Vienna (2012)Google Scholar
  14. 14.
    Sánchez López, M.A., Fernández Alemán, J.L., Toval, A., Carrillo de Gea, J.M.: Teléfonos inteligentes para la tercera edad: una revisión de aplicaciones móviles de salud (2015)Google Scholar
  15. 15.
    Stergiou, C., Psannis, K.E., Kim, B.G., Gupta, B.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)CrossRefGoogle Scholar
  16. 16.
    Suk, M., Prabhakaran, B.: Real-time mobile facial expression recognition system-a case study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 132–137 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sheila Bonilla
    • 1
    Email author
  • Enrique Moguel
    • 1
  • Jose Garcia-Alonso
    • 1
  1. 1.University of ExtremaduraCáceresSpain

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