Monitoring Viewer Attention During Online Ads

• URL: http://arxiv.org/abs/2504.06237v1
• Date: Tue, 08 Apr 2025 17:34:02 GMT
• Title: Monitoring Viewer Attention During Online Ads
• Abstract summary: Big brands gauge the liking and purchase intent of their new ads, by analyzing the facial responses of viewers recruited online to watch the ads from home or work.<n>Inattentive participants should get flagged and eliminated to avoid skewing the ad-testing process.<n>We introduce an architecture for monitoring viewer attention during online ads.
• Score: 0.5499796332553706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, video ads spread through numerous online platforms, and are being watched by millions of viewers worldwide. Big brands gauge the liking and purchase intent of their new ads, by analyzing the facial responses of viewers recruited online to watch the ads from home or work. Although this approach captures naturalistic responses, it is susceptible to distractions inherent in the participants' environments, such as a movie playing on TV, a colleague speaking, or mobile notifications. Inattentive participants should get flagged and eliminated to avoid skewing the ad-testing process. In this paper we introduce an architecture for monitoring viewer attention during online ads. Leveraging two behavior analysis toolkits; AFFDEX 2.0 and SmartEye SDK, we extract low-level facial features encompassing facial expressions, head pose, and gaze direction. These features are then combined to extract high-level features that include estimated gaze on the screen plane, yawning, speaking, etc -- this enables the identification of four primary distractors; off-screen gaze, drowsiness, speaking, and unattended screen. Our architecture tailors the gaze settings according to the device type (desktop or mobile). We validate our architecture first on datasets annotated for specific distractors, and then on a real-world ad testing dataset with various distractors. The proposed architecture shows promising results in detecting distraction across both desktop and mobile devices.

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