|
|
|
|
• Young people and students
• Museum visitors
• Tech and gamification enthusiasts
Italy
The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004545
• User-friendly interface – Easy adoption by museums and visitors
• Low start-up costs – Open-source tech eliminates licensing fees
• Minimal hardware requirements – Works on mid-range devices, reducing infrastructure costs
• BYOD compatibility – Visitors can use their own smartphones, reducing museum costs
• Gamified engagement – Increases visitor interaction, especially among younger audiences
• Customisation – Museums can tailor content and challenges to their collections and needs
• Privacy-friendly AI – Client-side processing ensures privacy and compliance with GDPR
Artificial vision systems and expression recognition are one of the focal points of the ethical debate on artificial intelligence and, not surprisingly, they represent one of the most closely monitored technologies within the new AI Act of the European Union. It is therefore essential for the museum, both ethically and legally, to clarify that no automatic identity recognition policy is implemented, nor is any record of the user's physical traits. The application should cover a broad range of physical traits. Finally, since this type of app uses images of artworks, it is important for the museum to properly manage the usage and sharing rights, whether the artworks belong to the museum or other museums. In line with these ethical considerations, the apps developed in the ReInHerit project are based on specific 'ReInHerit Ethics Cards,' developed by the Consortium partners to address various issues, such as the correct training of artificial intelligence, user data protection, and respect for the rights to the images of the artworks used. (20190317_museums-and-ai-toolkit_rl_web_ita_v2-1.pdf)
1. AI and Computer Vision for Facial Expression Matching
2. Hybrid Cloud-Edge Processing for Performance Optimization
3. Multi-Platform Compatibility
4. Gamification & Interactive UI
5. Privacy-Focused Digital Infrastructure
6. Digital Content Customisation & Museum Integration
7. Social Media & Digital Engagement Features
Face-fit app won a Best Demo Honorable Mention award at ACM Multimedia 2022, the foremost conference on multimedia.
1. Technical Skills
• Web Development
• AI and Machine Learning
• Database Management
• App Customisation and Deployment
2. Project Management Skills
• Planning & Coordination
• Budgeting & Resource Allocation
• Risk Management
3. Interpersonal Skills
• Communication & Collaboration
• Customer-Centric Mindset
• Stakeholder Engagement
4. Domain-Specific Expertise
• Cultural Heritage Knowledge
• Gamification & User Engagement
• Legal & Ethical Knowledge
5. Attitudes
• Openness to Innovation
• Adaptability
• Commitment to Sustainability
Face-Fit: AI-Powered Personalisation of Portraits
Face-Fit is an AI-based web application that allows users to replicate the pose and expression of historical portraits, transferring their face onto famous artworks. This generates a personalised image that can be downloaded and shared on social media. The application promotes a user-centred and gamified approach, transforming traditional museum visits into interactive experiences.
• Key Features:
The transferable innovative principles and methods of Face-fit include:
Face-Fit is an AI-driven, gamified web application designed to enhance museum engagement by allowing users to mimic the facial expressions and poses of historical artworks. The methodology combines:
• Pose-matching challenges guided by AI to personalize visitor interaction.
• Gamification principles that turn visitors into active participants rather than passive observers.
• Hybrid cloud-edge architecture that performs facial expression matching on users’ devices while offloading complex tasks to a remote server.
• Customisation by museums, who can upload their own collections and define challenges using an admin dashboard.
• Inclusive interaction design, such as torso-only options for wheelchair users.
• Privacy-first approach, avoiding personal data storage or biometric identification.
Visitors engage using their own devices (BYOD), which minimizes museum hardware costs and extends the experience through downloadable and shareable artwork-style images.
Core Resources:
• Web-based front-end (JavaScript) and backend (Python with Docker) – open-source, customizable.
• AI frameworks like TensorFlowJS (client-side) and OpenCV (server-side) – free and widely used.
• Server or cloud hosting for backend tasks (color correction, data handling) – ~€10–€50/month.
• Basic museum staff training for the admin dashboard – minimal once installed.
• Smartphones or tablets – users bring their own (BYOD model).
• Optional: Touchscreen kiosks or tablets for public use – ~€300–€1,000 each.
Estimated Start-up Cost:
• If BYOD model: ~€500–€2,000 (mostly for backend setup and deployment).
• If hardware provided by museum: ~€3,000–€5,000+, depending on the number of devices
Face-Fit’s design is inherently cost-effective, but here are further simplified options for small or local institutions:
• Use BYOD only: Avoid kiosk setups—visitors use their own smartphones with a QR code link to the app.
• Run on a shared hosting plan or free-tier cloud platform (e.g., Heroku, Render, or Replit).
• Leverage free AI models and pre-trained components (Face Mesh, MobileNetV2 via TensorFlowJS).
• Customize content using only museum-owned artworks with no external image licensing.
• Offer social sharing via downloadable image links without email integration, simplifying backend requirements.
Estimated Low-Cost Implementation:
• €0–€500, especially if you host on a free cloud tier and use open-source tools and templates.
This approach retains the interactive and gamified experience while remaining accessible for small museums, schools, or community-based heritage organizations.
