Museums Can Predict One Day’s Presence Through Facial Recognition

Engaging visitors has become critical in an era of data-driven museum management and marketing, as institutions work to expand and retain audiences while demonstrating public impact in order to secure funding. Museums use the data they collect to understand visitor intent and make content attractive and accessible to the wider public, translating the data into everything from personalized communications and interactive exhibition tours to “extra” programs 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 on the power of facial recognition technology to generate insights before shows go up. Rather than analyzing how people feel when they visit an exhibition, researchers focus on their initial emotional responses to advertising materials and what those responses can tell institutions about expectations, value perception and visit opportunities.

Today, facial recognition technology can capture small and nonverbal expressions, providing insight into people’s intentions and reactions as they encounter cultural content. Researchers can overcome biases based on self-reported data—that is, post-visit surveys—commonly used to assess visit intention.

Additionally, for decades, research on the museum experience has focused on aspects of cognition, language and space, from interpretive language to physical circulation. Emotional reactions, however, are increasingly recognized not as a secondary layer of experience but as a foundation in the study of cultural consumption behavior. Curiosity may spark interest, but emotional reactions strongly influence whether a person chooses 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 level of the experience directly influences the decision to revisit. Measuring emotions before a museum visit offers a strong complement to traditional exit surveys, which can be limited by recall bias and post-visit measurement.

In the face recognition test, participants were shown nine exhibition posters taken from museum websites, representing focused installations and exhibitions focused on classical and historical painting. The first phase of testing confirmed that images can produce measurable emotional responses, especially happiness. The main study was designed to ensure authenticity and increase scientific rigor: each picture was shown on a high-definition screen for 18 seconds, followed by a short blank interval to prevent visual carryover effects and mental 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 measures happiness levels on a continuous scale. In order to capture the purpose of the visit without relying on verbal descriptions, participants used thumb or pointing gestures to record natural responses, reducing the bias introduced by written or spoken responses.

The findings showed that happiness can be a strong predictor of the intention to visit. At medium to high levels, excitement appeared to be a reasonable indicator of genuine interest. Interestingly, greater happiness was not always directly related to intention, suggesting that emotional intensity alone does not automatically translate into action. There were demographic differences: younger participants showed a stronger connection between emotional reactions and travel intention, suggesting that emotions play a greater role in decision-making. Women also show a stronger correlation between emotional reactions and intentions. Overall, the findings are consistent with research showing that younger audiences are particularly attracted to cultural experiences that involve emotion and concentration, while older visitors may prioritize other aspects.

For museums seeking to attract new generations, understanding the emotional motivations of prior visits can be a game-changer. Can facial recognition tools, especially when combined with AI, help museums refine marketing strategies by identifying which images and narratives generate the strongest emotional engagement even before a visit? In theory, using this technology will allow institutions to identify which visual and narrative elements provide the strongest emotional responses, thereby improving marketing campaigns and creating engaging displays.

Although research has focused primarily on pre-visit time, potential applications extend beyond advertising. Emotion analysis based on the facial recognition of visitors in the chair can inform exhibition design and visitor flow, allowing museums to tailor pathways to areas of strong emotional engagement and remove mediators in a meaningful way. Such tools may also support more personal interactions after the visit, enabling establishments to recommend future exhibitions that match visitors’ emotional responses and strengthen engagement and long-term retention.

The potential use of these technologies by museums raises important ethical and privacy considerations, of course. In an era of increasing surveillance and government use of biometric data, their transmission needs to be carefully controlled and transparent. The GDPR already sets clear requirements regarding consent and disclosure, and public acceptance depends largely on how these tools are used and communicated. The challenge for museums will be to use these technological opportunities responsibly, ensuring transparency and maintaining visitor autonomy and anonymity while unlocking their potential to deepen engagement and expand cultural access.

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