AutomotiveVideo input
Receive suitable facial video from a cabin camera or evaluation camera.
PIC platform
From cabin video to physiological signal.
PIC applies computer vision and physiological modelling to subtle temporal changes in facial video, producing an estimated signal together with the quality information needed to interpret it responsibly.
Signal pipeline
A software layer, designed around automotive constraints.
The algorithm is only one part of the system. Image formation, illumination, compression, motion, face coverage and compute placement all affect whether a useful signal is available.
Frame assessment
Assess face visibility, motion, lighting and other conditions relevant to signal extraction.
Physiological model
Estimate heart-rate-related information from temporal visual patterns.
Signal + confidence
Expose the estimate alongside quality and availability for downstream fusion.
Dynamic monitoring
Real vehicles make physiological sensing harder.
Camera-based physiological monitoring depends on subtle temporal information. Driving adds motion, vibration, rapid illumination changes and image-pipeline effects that can obscure the signal.
Driver and vehicle movement
Head turns, facial movement, road vibration and relative camera motion introduce artefacts that must be separated from physiological change.
Changing illumination
Sun, shadow, tunnels, night lighting and reflections alter exposure and spectral conditions from moment to moment.
Signal preservation
Frame timing, exposure, gain, denoising, compression and ISP processing can preserve—or remove—the information required for estimation.
Steady environment
Observed heart-rate accuracy under evaluated steady conditions.
Real driving conditions
Observed across evaluated driving conditions varying in speed, road surface and lighting.
Performance figures describe evaluated conditions. Results depend on the camera, image pipeline, subject visibility and operating environment; target-platform validation remains essential.
Model approach
Built from physiological video intelligence—not a generic face feature.
FaceMed’s broader platform experience in multimodal clinical AI informs the discipline behind PIC: temporal modelling, large-scale physiological video learning, reviewable output and explicit limits.
Signals evolve over time
PIC analyses temporal visual information rather than a single still image.
Availability is an output
A useful automotive component must be able to communicate uncertainty and non-availability.
Integration changes performance
Every sensor, illumination and ISP combination requires evidence on the target platform.
Integration boundary
A physiological input—not an independent vehicle decision.
PIC has a defined role in the cabin stack. It processes suitable facial video and exposes an estimate with quality information; the OEM or integrator owns fusion, HMI, fallback and response policy.
Camera stream
Facial video, timing metadata and an agreed image pipeline.
Physiological engine
Quality assessment, inference and signal-availability logic.
SDK or API
Estimated signal, trend, confidence and availability fields.
Fusion and policy
Behavioural fusion, verification, HMI, fallback and response.
Validation & evidence
Match every claim to the evidence behind it.
Signal accuracy, vehicle robustness, application validity and production assurance answer different questions. A credible programme establishes them in sequence.
Signal feasibility
Compare the physiological estimate against an agreed reference.
Vehicle robustness
Test motion, vibration, illumination and occupant variation.
Use-case validity
Establish whether the signal improves the defined workflow.
Production assurance
Address programme, safety, cybersecurity, privacy and regulatory requirements.
Reference comparison
Error distribution, agreement bands and results broken down by protocol and condition.
Signal quality
Usable time, time to estimate, degraded states and recovery after difficult conditions.
Failure modes
Transparent analysis of motion, light, occlusion, face coverage, camera and ISP sensitivity.
Bring us your camera pipeline.
We can start with a structured review of sensor, illumination, image processing, compute and target use case.