Museums May One Day Predict Cultural Participation Through Facial Recognition

Visitor engagement has become essential in the age of data-driven museum management and marketing, as institutions work to expand and retain audiences while demonstrating social impact to secure funding. Museums use the data they collect to understand visitors’ intentions and make content more compelling and accessible to a broader public, translating insights into everything from tailored communications and interactive exhibition journeys to programs of “extras” that can include classes, concerts, lectures and film screenings.

A new study by Charlotte De Sainte Maresville and Christine Petr, published in the International Journal of Arts Management, focuses specifically on the potential of facial recognition technology to generate insights before exhibitions go up. Rather than analyzing how people feel when visiting a show, the researchers focused on their initial emotional reactions to promotional materials and what those responses could tell institutions about expectations, perception of value and likelihood of visitation.

Today, facial recognition technologies can capture microexpressions and nonverbal cues, offering insight into people’s intentions and reactions as they encounter cultural content. Researchers can bypass the rationalization biases inherent in self-reported data—namely, post-visit surveys—traditionally used to assess visit intention.

Additionally, for decades, museum experience research has focused on cognitive, linguistic and spatial factors, ranging from interpretive language to physical circulation. Emotional response, however, is increasingly recognized not as a secondary layer of experience but as foundational in the study of cultural consumption behaviors. Curiosity may spark interest, but emotional reactions strongly influence whether an individual will opt to engage with cultural content.

Positive emotional states such as enthusiasm and curiosity have been shown to increase participation in cultural activities, while the emotional quality of an experience directly influences the decision to revisit. Measuring emotion before a museum visit offers a powerful complement to traditional exit surveys, which can be limited by memory bias and post-visit rationalization.

In the facial recognition experiment, participants were shown nine promotional exhibition posters drawn from museum websites, representing immersive installations and shows focused on classical painting and history. An initial exploratory phase confirmed that the images could generate measurable emotional responses, particularly joy. The main study was designed to ensure realism and maximize scientific rigor: each image was displayed on a high-resolution screen for 18 seconds, followed by a brief blank interval to prevent visual carryover effects and cognitive fatigue. All sessions took place in a controlled university environment, with informed consent obtained in full compliance with GDPR and ethical research standards.

Emotional responses were measured using FaceReader software, which analyzes facial expressions and quantifies levels of joy on a continuous scale. To capture visit intention without relying on verbal explanations, participants used a thumbs-up or thumbs-down gesture to record instinctive reactions, minimizing bias introduced by written or spoken responses.

The findings showed that joy can be a strong predictor of intention to visit. At moderate to high levels, joy proved to be a meaningful indicator of genuine interest. Interestingly, extremely intense joy did not always correlate directly with intention, suggesting that emotional intensity alone does not automatically translate into action. There were demographic differences: younger participants showed a stronger link between emotional response and visit intention, suggesting that emotion plays a greater role in their decision-making. Women also showed a stronger correlation between emotional reactions and intentions. Overall, the findings align with research indicating that younger audiences are especially drawn to emotionally engaging and immersive cultural experiences, while older visitors may prioritize other factors.

For museums seeking to attract new generations, understanding pre-visit emotional triggers could be a game-changer. Could facial recognition tools, especially when combined with A.I., help museums refine marketing strategies by identifying which images and narratives generate the strongest emotional engagement even before a visit? In theory, applying this technology would allow institutions to identify which visual and narrative elements elicit the strongest emotional responses, thereby optimizing marketing campaigns and designing more engaging exhibition experiences.

Although the study focuses primarily on the pre-visit moment, the potential applications extend beyond marketing. Facial-recognition-based emotional analysis of visitors in situ could inform exhibition design and visitor flow, allowing museums to adjust pathways toward areas of stronger emotional engagement and deploy mediators more strategically. Such tools could also support more personalized post-visit communication, enabling institutions to recommend future exhibitions aligned with visitors’ emotional responses and strengthen long-term engagement and retention.

The potential use of these technologies by museums raises important ethical and privacy considerations, of course. In an era of growing scrutiny and government use of biometric data, their deployment requires careful regulation and transparency. The GDPR already imposes clear requirements regarding consent and disclosure, and public acceptance depends largely on how these tools are implemented and communicated. The challenge for museums will be to leverage these technological opportunities responsibly, ensuring transparency and preserving visitors’ autonomy and anonymity while unlocking their potential to deepen engagement and expand cultural access.

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