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Using Software to Code Facial Expressions of Emotion Kristin Smith-Crowe | University of Utah Jaime M. I. Potter and Sigal G. Barsade | University of Pennsylvania.

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Presentation on theme: "Using Software to Code Facial Expressions of Emotion Kristin Smith-Crowe | University of Utah Jaime M. I. Potter and Sigal G. Barsade | University of Pennsylvania."— Presentation transcript:

1 Using Software to Code Facial Expressions of Emotion Kristin Smith-Crowe | University of Utah Jaime M. I. Potter and Sigal G. Barsade | University of Pennsylvania Technical Assistance: Robert Botto, Programmer/Analyst, Senior IT Project Leader 2014 Academy of Management Conference | Unobtrusive Measures Professional Development Workshop

2 Background on Study  We are conducting research on emotional contagion.  We video recorded participants’ faces while they watched a video stimulus.  The study took place in a computer lab equipped with desktop computers.  Participants were recorded via a webcam.  The video stimulus was embedded in a Qualtrics survey.  The audio was delivered via headphones Academy of Management Conference | Unobtrusive Measures Professional Development Workshop

3 Alignment  An important issue was the alignment of the start of the video stimulus with the start of the recording of participants.  We needed to know exactly when the participant saw what so that we could test hypotheses about the reactions of participants to particular content.  We hoped to find a way to automate the simultaneous start of both, but we weren’t able to do so.  Instead, we found a low-fi solution that entailed collecting the data in such a way as to allow us to manage the alignment post data collection Academy of Management Conference | Unobtrusive Measures Professional Development Workshop

4 Alignment  During the Data Collection  We inserted a page in our Qualtrics survey telling participants to raise their hands to call over an RA.  The RA then began a screen capture program, the webcam, and then the video stimulus.  We used Free Screen to Video V 2.0 to create a video of the computer screen. This allowed us to record when the video recording of the participant began and when the video stimulus began.  Post Data Collection  For each participant, we watched the screen capture videos in Aegisub to mark the times that the video stimulus began and the video recording of the participant began.  The precision of the timer in Ageisub allowed for greater precision in alignment Academy of Management Conference | Unobtrusive Measures Professional Development Workshop

5 Analysis of Emotion Data  Once alignment was achieved, we used Noldus FaceReader 5 to analyze participants’ facial expressions.  This software can analyze live feeds or videos.  It detects the type and intensity of seven categories of expressions.  Happy, sad, angry, surprised, scared, disgusted, and neutral  Click on the pictures below to see examples of FaceReader analyses: 2014 Academy of Management Conference | Unobtrusive Measures Professional Development Workshop

6 Output Files  An example of the output appears below. FaceReader produces one text file per participant.  Setting FaceReader to analyze 30 frames per second produces a lot of data. In this case, there are 720 rows of data per participant Academy of Management Conference | Unobtrusive Measures Professional Development Workshop Note: These data are fictional.

7 Output Files Converted to Excel  Once we had all of the output files, we merged them into an Excel file. Each row is a point in time and the numbers (e.g., Neutral1) refer to a participant (e.g., Participant 1).  We are using the Excel file to figure out how to aggregate the data as we have 5,040 data points per participant Academy of Management Conference | Unobtrusive Measures Professional Development Workshop Note: These data are fictional.

8 Output Files Converted to SPSS  Once we aggregate the data, we will create an SPSS file that will look something like this (where T1 = time 1 and so forth). This type of format will allow us to test our hypotheses Academy of Management Conference | Unobtrusive Measures Professional Development Workshop Note: These data are fictional.

9 Tips  Collect race and age data.  The general model does not work as well for East Asian people, the elderly, and children. There are models that are particular to these populations.  If you have such data, you can make sure that you aren’t seeing systematic missing data due to race and age.  FaceReader 6 is now available.  It features an improved East Asian model; the capacity to detect contempt; and the capacity to analyze expressions based on the circumplex model of affect Academy of Management Conference | Unobtrusive Measures Professional Development Workshop

10 Technology Used Screen Capture Program Free Screen to Video V 2.0, Koyote Software Survey PlatformQualtrics Video Playback ProgramAegisub Emotion Analysis Program Noldus FaceReader 5 +white paperwhite paper 2014 Academy of Management Conference | Unobtrusive Measures Professional Development Workshop Note: We also used Excel and SPSS.


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