Digital Signage

Face It!: Reading Human Facial Cues

Signage systems using AI and video cameras enable emotion and demographic-based analytics.

Audience measurement is the constant mantra of brand marketers and data analytics solution providers. Although there are numerous platforms for Big Data analysis, only a few now feature algorithms for analyzing human reactions to digital signage content with the use of a video camera to capture viewer responses. Artificial intelligence and other emerging technologies are making it possible to analyze the viewer’s age, gender, attention span and even emotional reactions. These advanced systems can be programmed to respond to the viewer appropriately, even personalizing content.

Many retailers and advertisers are excited about the potential of these new technologies, but some hesitate to use them due to privacy concerns. Although some futurists propagate the notion that the word “privacy” is no longer relevant, it may take many years before this is widely accepted. The main issue, however, is a lack of understanding of how “facial detection” and “facial recognition” platforms differ. That’s why it is necessary for system designers and integrators to be able to articulate the differences to their clients, explaining the benefits and dispelling their fears.

Some of the nuance lies in the marketing literature of data analytics platforms, which use terms such as emotion-sensing, audience-aware or mood-estimator. When Intel started marketing its Audience Impression Metric Suite years ago, it made it clear that the platform “anonymously” counted viewers and analyzed them by gender and age range, in real time. Other data metrics system providers in the digital signage market also emphasize the anonymity of viewers captured by the camera. In recent years, AdMobilize, Affectiva and Quividi have been among the companies that offer “intelligent” platforms, and also provide descriptions of the “classifiers” that distinguish their solutions.

Jerry Reese, VP of Sales for Creative Realities, a digital solutions integration firm that tests available solutions before making recommendations to clients, said, in a phone interview, “Our role is to help our clients understand the full capabilities and the limitations of the system prior to deployment. It is an exercise in educating them on the differences, for example, between face detection and face recognition.” Reese also cautioned that, “Some Anonymous Video Analytics (AVA) metrics have inherent accuracy limitations, such as measurements of emotion and mood. Therefore, we ensure our clients understand that such data can provide an indication of patterns and be used to guide content decisions, but should not be used as an absolute metric for individual persons.”

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