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Abstract
User authentication and feedback gathering are crucial aspects for recommender systems. The most common implementations, a username / password login and star rating systems, require user interaction and a cognitive effort from the user. As a result, users opt to save their password in the interface and optional feedback with a star rating system is often skipped, especially for applica- tions such as video watching in a home environment. In this article, we propose an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics. Using facial recognition with a camera in a tablet, smartphone, or smart TV, the persons in front of the screen can be identified in order to link video watch- ing sessions to their user profile. During video watching, implicit feedback is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An emotion finger- print, which is defined as a unique spectrum of expected emotions for a video scene, is compared to the recognized emotions in order to estimate the experience of a user while watching. An evaluation with a test panel showed that happiness can be most accurately detected and the recognized emotions are correlated with the user’s star rating.

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Citation

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MLA
De Pessemier, Toon, et al. “Using Facial Recognition Services as Implicit Feedback for Recommenders.” Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems Co-Located with 13th ACM Conference on Recommender Systems(RecSys 2019), edited by Peter Brusilovsky et al., vol. 2450, CEUR, 2019, pp. 28–35.
APA
De Pessemier, T., Coppens, I., & Martens, L. (2019). Using facial recognition services as implicit feedback for recommenders. In P. Brusilovsky, M. de Gemmis, A. Felfernig, P. Lops, J. O’Donovan, G. Semeraro, & M. C. Willemsen (Eds.), Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with 13th ACM Conference on Recommender Systems(RecSys 2019) (Vol. 2450, pp. 28–35). Copenhagen, Denmark: CEUR.
Chicago author-date
De Pessemier, Toon, Ine Coppens, and Luc Martens. 2019. “Using Facial Recognition Services as Implicit Feedback for Recommenders.” In Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems Co-Located with 13th ACM Conference on Recommender Systems(RecSys 2019), edited by Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O’Donovan, Giovanni Semeraro, and Marijn C. Willemsen, 2450:28–35. CEUR.
Chicago author-date (all authors)
De Pessemier, Toon, Ine Coppens, and Luc Martens. 2019. “Using Facial Recognition Services as Implicit Feedback for Recommenders.” In Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems Co-Located with 13th ACM Conference on Recommender Systems(RecSys 2019), ed by. Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O’Donovan, Giovanni Semeraro, and Marijn C. Willemsen, 2450:28–35. CEUR.
Vancouver
1.
De Pessemier T, Coppens I, Martens L. Using facial recognition services as implicit feedback for recommenders. In: Brusilovsky P, de Gemmis M, Felfernig A, Lops P, O’Donovan J, Semeraro G, et al., editors. Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with 13th ACM Conference on Recommender Systems(RecSys 2019). CEUR; 2019. p. 28–35.
IEEE
[1]
T. De Pessemier, I. Coppens, and L. Martens, “Using facial recognition services as implicit feedback for recommenders,” in Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with 13th ACM Conference on Recommender Systems(RecSys 2019), Copenhagen, Denmark, 2019, vol. 2450, pp. 28–35.
@inproceedings{8636352,
  abstract     = {User authentication and feedback gathering are crucial aspects
for recommender systems. The most common implementations, a
username / password login and star rating systems, require user
interaction and a cognitive effort from the user. As a result, users
opt to save their password in the interface and optional feedback
with a star rating system is often skipped, especially for applica-
tions such as video watching in a home environment. In this article,
we propose an alternative method for user authentication based
on facial recognition and an automatic feedback gathering method
by detecting various face characteristics. Using facial recognition
with a camera in a tablet, smartphone, or smart TV, the persons in
front of the screen can be identified in order to link video watch-
ing sessions to their user profile. During video watching, implicit
feedback is automatically gathered through emotion recognition,
attention measurements, and behavior analysis. An emotion finger-
print, which is defined as a unique spectrum of expected emotions
for a video scene, is compared to the recognized emotions in order
to estimate the experience of a user while watching. An evaluation
with a test panel showed that happiness can be most accurately
detected and the recognized emotions are correlated with the user’s
star rating.},
  author       = {De Pessemier, Toon and Coppens, Ine and Martens, Luc},
  booktitle    = {Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with 13th ACM Conference on Recommender Systems(RecSys 2019)},
  editor       = {Brusilovsky, Peter and de Gemmis, Marco and Felfernig, Alexander and Lops, Pasquale and O’Donovan, John and Semeraro, Giovanni and Willemsen, Marijn C.},
  issn         = {1613-0073},
  language     = {eng},
  location     = {Copenhagen, Denmark},
  pages        = {28--35},
  publisher    = {CEUR},
  title        = {Using facial recognition services as implicit feedback for recommenders},
  volume       = {2450},
  year         = {2019},
}