Automated Facial Analysis for Digital Media Response Measurement
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Solution Overview
Problem
The challenge in assessing the effectiveness of digital media content lies in measuring consumer responses to dynamic advertisements, as existing methods struggle to accurately capture changes in mental states and attention across multiple screens with complex, continuously changing content.
Innovation Solution
An automated system using computer vision and machine learning to analyze facial expressions and gaze direction, translating emotional responses into spatiotemporal emotional response maps to provide detailed ratings on media content effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used to evaluate digital media effectiveness, then individual content analysis can be performed, but the assessment process becomes extremely time-consuming and labor-intensive due to the sheer number of displays and complex programming content
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated computer vision system that uses machine learning algorithms to analyze facial expressions and gaze patterns. This substitution enables the system to process multiple screens and complex content simultaneously, dramatically reducing assessment time while maintaining or improving measurement precision through consistent, objective analysis of consumer responses.
2Loss of information
If comprehensive measurement of consumer mental states is attempted, then deeper insight into consumer response can be obtained, but the complexity of the measurement system increases significantly
Solution Approach 1:
The patent introduces facial expressions and gaze patterns as intermediary indicators that indirectly reflect consumer mental states and attention. Rather than attempting to directly measure complex internal cognitive processes, the system uses these observable external cues as mediators, simplifying the measurement approach while still capturing valuable information about consumer response to digital media content.
Solution Approach 2:
The patent employs machine learning algorithms and computer vision technology to automatically interpret facial expression and gaze data, replacing complex manual analysis procedures. This automated approach reduces system complexity by integrating multiple measurement functions into a unified software-based solution that can process and interpret consumer response data without requiring complex hardware interventions.
3Measurement precision
If analysis of individual sub-content elements is performed across the entire media programming, then detailed effectiveness data can be obtained, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the media content into discrete temporal and spatial units, allowing the system to analyze consumer responses to individual sub-content elements independently. By dividing the overall media programming into manageable segments and tracking consumer attention and emotional responses to each segment separately, the system achieves detailed measurement precision without overwhelming computational complexity, as each segment can be processed independently through the automated analysis pipeline.
Data Source
AI summary
The present invention is a method and system to provide an automatic measurement of people's responses to dynamic digital media, based on changes in their facial expressions and attention to specific content. First, the method detects and tracks faces from the audience. It then localizes each of the faces and facial features to extract emotion-sensitive features of the face by applying emotion-sensitive feature filters, to determine the facial muscle actions of the face based on the extracted emotion-sensitive features. The changes in facial muscle actions are then converted to the changes in affective state, called an emotion trajectory. On the other hand, the method also estimates eye gaze based on extracted eye images and three-dimensional facial pose of the face based on localized facial images. The gaze direction of the person, is estimated based on the estimated eye gaze and the three-dimensional facial pose of the person. The gaze target on the media display is then estimated based on the estimated gaze direction and the position of the person. Finally, the response of the person to the dynamic digital media content is determined by analyzing the emotion trajectory in relation to the time and screen positions of the specific digital media sub-content that the person is watching.