Jonathan Brem
ABSTRACT
In this paper, we propose an Android-based assistance system called SmileAtMe which uses smile detection as a means of direct interaction in order to rate meme images. A smartphone's front camera captures the user's face while they are interacting with the application on the device. A system evaluation uncovered that users fully understand how the application uses smile detection to rate images. The users attested that the automatic reaction classification works well. However, several of them were uncomfortable with the idea of being observed by their smartphone.
- 9GAG. 2016. 9GAG -- Go Fun Yourself. Retrieved March 26, 2016 from http://www.9gag.comGoogle Scholar
- Roddy Cowie, Ellen Douglas-Cowie, Nicolas Tsapatsoulis, George Votsis, Stefanos Kollias, Winfried Fellenz and John G. Taylor. 2001. Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine 18, 1: 32--80.Google ScholarCross Ref
- Oscar Déniz, Modesto Castrillón Santana, Javier Lorenzo-Navarro, Luis Antón-Canalís and Gloria Bueno. 2008. Smile Detection for User Interfaces. In Advances in Visual Computing, George Bebis et al. (eds.). Springer Berlin Heidelberg, Berlin / Heidelberg, 602--611. Google ScholarDigital Library
- Google. 2015. Mobile Vision | Google. Retrieved March 26, 2016 from https://developers.google.com/vision/Google Scholar
- HUGELOL. 2016. HUGELOL -- About. Retrieved March 26, 2016 from http://hugelol.com/moreGoogle Scholar
- Michele Knobel and Colin Lankshear. 2007. Online memes, affinities, and cultural production. In A new literacies sampler, Michele Knobel and Colin Lankshear (eds.). Peter Lang Publishing, New York, 199--227.Google Scholar
Index Terms
- SmileAtMe: rating and recommending funny images via smile detection
Recommendations
Personalized Recommendation of Socially Relevant Images
WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and SemanticsWe present a social image recommender system that offers a hybrid filtering approach, combining content- and knowledge-based filtering with a novel social-based filtering, that selects images of social interest to the user, by e.g. being posted by close ...
Multimodal collaborative graph for image recommendation
AbstractRecent works for personalized recommendation typically emphasize their efforts on learning users’ interests from interactions. However, users make decisions depending on multiple factors, especially various attributes of items like appearance, ...
Cross-modal Collaborative Manifold Propagation for Image Recommendation
ICMR '19: Proceedings of the 2019 on International Conference on Multimedia RetrievalWith the rapid evolution of social networks, the increasing user intention gap and visual semantic gap both bring great challenge for users to access satisfied contents. It becomes promising to investigate users' customized multimedia recommendation. In ...
Comments