Make It Feel: Use of Facial Imaging Technique to Analyze the Impact of Each Emotional Spot on Ad Success

we propose to lay the foundation of an automated method for classifying impact of various emotional spots on advertisement success. This approach is based on analyzing various muscles activated while making a facial expression in response to certain ad. Each emotion has its own impact on consumers and every emotional spot can help to increase sales. Certain types of emotion are better able to fulfill the purpose and meet the goal of persuading targeted consumers for buying a product than others. The challenge is to analyze which emotion creates consumer engagement with the product and deliver that emotion for creating a brand image. This is a very challenging pattern recognition problem that requires robust detection of various natural, spontaneous expressions and their intensities in real time environment. This paper demonstrates the possibility for an ecologically valid, subtle evaluation of advertisement success, strong speculation of brand building based on facial responses. The framework can be successfully implemented impersonalizing online ads that are shown to viewers while watching media over the Internet or television and in concept testing in order to know what customers' values and what they don't.

[1]  Paul D. Bolls,et al.  The Effects of Message Valence and Listener Arousal on Attention, Memory, and Facial Muscular Responses to Radio Advertisements , 2001, Commun. Res..

[2]  P. Ekman,et al.  What the face reveals : basic and applied studies of spontaneous expression using the facial action coding system (FACS) , 2005 .

[3]  Takeo Kanade,et al.  Facial Expression Analysis , 2011, AMFG.

[4]  N. Srinivasan,et al.  Role of affect in decision making. , 2013, Progress in brain research.

[5]  Fernando De la Torre,et al.  Temporal Segmentation of Facial Behavior , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  Rama Chellappa,et al.  Face Processing: Advanced Modeling and Methods , 2006, J. Electronic Imaging.

[7]  Fernando De la Torre,et al.  Facial Expression Analysis , 2011, Visual Analysis of Humans.

[8]  Jianhou Gan,et al.  Feature Extraction based on Local Directional Pattern with SVM Decision-level Fusion for Facial Expression Recognition , 2013 .

[9]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

[10]  Massimiliano Pontil,et al.  Support Vector Machines: Theory and Applications , 2001, Machine Learning and Its Applications.

[11]  K. Poels,et al.  How to Capture the Heart? Reviewing 20 Years of Emotion Measurement in Advertising , 2006 .

[12]  Thomas S. Huang,et al.  A PIECEWISE BÉZIER VOLUME DEFORMATION MODEL AND ITS APPLICATIONS IN FACIAL MOTION CAPTURE , 2002 .

[13]  Daniel McDuff,et al.  Predicting online media effectiveness based on smile responses gathered over the Internet , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[14]  R. Pieters,et al.  Emotion-Induced Engagement in Internet Video Advertisements , 2012 .

[15]  Preeti Saraswat,et al.  Temporal Segmentation of Facial Behavior in Static Images Using HOG & Piecewise Linear SVM , 2014 .

[16]  Paul J. Lavrakas,et al.  “Does Anyone Really Know If Online Ad Campaigns Are Working?” , 2010, Journal of Advertising Research.

[17]  Maja Pantic,et al.  Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Gwen Littlewort,et al.  Analysis of Machine Learning Methods for Real-Time Recognition of Facial Expressions from Video , 2003 .

[19]  P. McKay.  A Review of “Brand Immortality: How Brands Can Live Long and Prosper” , 2010 .

[20]  Richard L. Hazlett,et al.  Emotional response to television commercials: Facial EMG vs. self-report , 1999 .