As organizations worldwide emphasize data privacy, rapid response, and cost concerns, cloud-based face-recognition systems are being viewed more negatively. Possible network downtime, ongoing cloud costs, and stringent requirements for data sovereignty make a solid case for processing biometric data locally. IndoAI Technologies builds a solution that addresses these issues at the source by providing a complete face-recognition pipeline within its edge-AI camera.
Before looking into IndoAI’s approach, it helps to understand why many organizations seek alternatives to cloud processing:
Most cloud-based solutions use a model of streaming video to remote servers (often hundreds or thousands of miles away) for analysis, which can introduce delays and potentially great undo security operations. If an unauthorized individual were to enter a secured location, every millisecond counts. A transient fault in the network could allow the intruder to enter unnoticed.
IndoAI’s edge‑AI lineup includes three camera tiers—Core, Edge, and Pro—each optimized for different scale and performance needs:
Model | AI Capacity | Typical Deployments |
---|---|---|
Core | Single model (e.g., face recognition) | Small offices, retail outlets |
Edge | Up to three concurrent models | Medium enterprises, campus entrances |
Pro | Up to five concurrent models | Large industrial complexes, data centers |
A simple convolutional network iterates over each frame of a video to find faces. Prior to feeding each region with a face into the next stage, we normalize the regions by scaling, color balancing and contrasting to provide uniform inputs.
The system utilizes a compact embedding network (similar to MobileFaceNet) to map each face to fixed-length feature vector. The model optimizations employ quantization and pruning to minimize the inference footprint and preserve accuracy.
Rather than offload to the cloud, the camera keeps an encrypted database of face embeddings on device. If a new face comes up, its vector is against stored identities with cosine similarity as the measure. The threshold for matching stored identities is configurable to a balance of security and usability.
As soon as a match (or unknown face) is confirmed, the camera executes predefined actions in real time; unlock doors, trigger alarms, send notifications via SMS and/or mobile apps. Each event has a time stamped and stored locally, with the option to batch sync the data back to a central dashboard.
To protect sensitive biometric data, IndoAI implements multiple layers of security:
Although face recognition occurs entirely on-device, IndoAI provides flexible management options:
IndoAI cameras are transforming access control in manufacturing plants to be badge-less and hands free while also providing accurate shift logs. On corporate campuses, automated attendance reporting goes directly into payroll and HR systems, entirely removing the potential for human error from manual entry. Retail chains are using VIP recognition to enhance customer service while institutions of learning are securing campaus perimeter protection and visitor access – with not a single frame leaving the device!
IndoAI offers a family of AI cameras and compute modules designed specifically for on‑device inference:
IndoAI continues to innovate, with upcoming features including:
IndoAI has embedded an entire face-recognition pipeline into its edge-AI cameras and eliminates the downsides of being cloud-dependent—ie, instant alerts, data sovereignty, and operational cost savings. Others (beyond IndoAI) are delivering reliable, real-time biometric authentication in various industries with their proven and reliable hardware, optimized AI models, and security and privacy maturities. In an era of device-based inference, How IndoAI Does Face Recognition Without the Cloud is a yardstick for secure, efficient, and truly scalable face-recognition.
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