Traditional facial recognition CCTV systems often operate in silos, creating islands of information, but Solution House aims to change that with a multi-tenant platform designed for smarter, collaborative security.
While conventional single-tenant facial recognition platforms restrict data access to individual clients, Solution House has introduced a multi-tenant architecture that promotes data sharing among stakeholders, transforming the way facial recognition technology supports security operations.
“This many-to-many approach enables neighbouring properties, managed by different security providers, to share relevant data in a POPIA-compliant manner and break the bonds of isolated data management,” says Tiaan Janse van Rensburg, Director at Solution House Software.
Collaborative intelligence for enhanced security
The Solution House facial recognition platform, FaceCamAlert, is built to operate across multiple tenants, allowing property managers, private security firms and even law enforcement agencies to access shared data streams. This capability significantly enhances situational awareness and response capabilities.
Property managers can co-ordinate security efforts across multiple sites and security providers and benefit from improved incident detection and prevention, and SAPS can more easily identify and apprehend repeat offenders operating across different locations.
Focus on POPIA-compliant data handling
With the increasing scrutiny around facial recognition technologies, Solution House prioritises Protection of Personal Information Act (POPIA) compliance through a multi-pronged strategy:
- Immediate data disposal: Individuals who do not match any records are excluded from the system, with their data deleted immediately to protect privacy.
- Built-in POPIA features: The platform includes tools for classification, automated POPIA notices and data separation between individuals and incidents.
- Subscriber controls: Data sharing is governed by strict rules, with subscribers able to control what is shared and with whom.
- Legal support: Users gain access to legal frameworks, consent documentation and signage templates to stay compliant.
“All facial recognition data is encrypted end-to-end – from camera to matching engine – and hosted in secure, managed data centres,” says Janse van Rensburg.
Customisable, scalable and efficient
Built with a scalable matching engine and micro-services architecture, the platform supports real-time facial recognition across multiple properties. Edge computing enables local processing of video feeds, reducing latency and bandwidth usage. Only images with a high match threshold are escalated for verification.
The FaceCamAlert platform currently integrates with CCTV infrastructure, particularly newer open-platform models from Dahua and Hikvision, with Axis being added to the supported brands later in 2025.
Subscribers can manage access controls, define watchlists based on property type and contact original data uploaders when a potential match is found outside their access scope.
Real-world results and future focus
FaceCamAlert is already deployed across high-risk environments such as shopping centres, office parks and security estates, with several serious criminal arrests – including murder, robbery and fraud – attributed to its use.
“The retail sector has seen the highest number of successful matches and interventions,” says Janse van Rensburg.
Advice for prospective users
Janse van Rensburg advises organisations to define their facial recognition objectives clearly and ensure the proper legal and procedural groundwork is in place.
“Choose a solution that is POPIA-compliant, scalable and supports collaboration with neighbouring properties. Most importantly, it should be easy to implement and expand over time,” he concludes.
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