Issue No. 04 - Oct.-Dec. (2014 vol. 5)
Songfan Yang , Center for Research in Intelligent Systems, University of California, Riverside
Mehran Kafai , Hewlett Packard Laboratories, Palo Alto
Le An , Center for Research in Intelligent Systems, University of California, Riverside
Bir Bhanu , Center for Research in Intelligent Systems, University of California, Riverside
In marketing and advertising research, “zapping” is defined as the action when a viewer stops watching a commercial. Researchers analyze users’ behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers’ zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user’s zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers.
Advertising, Videos, Indexes, Data collection, Face recognition, Measurement, Internet
S. Yang, M. Kafai, L. An and B. Bhanu, "Zapping Index:Using Smile to Measure Advertisement Zapping Likelihood," in IEEE Transactions on Affective Computing, vol. 5, no. 4, pp. 432-444, 2014.