Biometric Video Intelligence
IDENTIFY.TRACK.RECONSTRUCT.
Open-source platformMulti-modal recognitionReal-time & forensic analysis
Beyond the Limits of Facial Recognition
- faces are masked or obscured
- lighting is poor
- video quality is low
99.9%+ Accuracy
- 120+ static parameters
- 40+ dynamic features
- Reliability on par with facial recognition
Admissible Evidence
- Identified a suspect in a real murder case in the EU
- Validated, explainable AI models
- Match scores based on up to 100+ objective metrics
How Gait & Body Recognition Works
From Video to Actionable Intelligence
Real-World Applications
Forensic / Law Enforcement
- Identify individuals from low-quality, distant, or night-time video
- Match suspects when faces are hidden or obscured
- Produce explainable, defensible results for court use
- Build suspect shortlists and connect crime scenes from large video datasets
Security / Access Control
- Re-ID individuals across multiple cameras and locations
- Maintain tracking when faces are not visible
- Add a second biometric layer to existing access control systems
- Detect and follow persons of interest in real time
Biomechanics / Medical
- Extract high-dimensional movement and body data from video
- Analyze gait, posture, and motion patterns at scale
- Enable research, diagnostics, and performance optimization
- Replace expensive motion capture setups with video-based analysis
Forensic Investigations
for Law Enforcement & Post-Incident Analysis
Turn Footage Into Evidence
When facial recognition fails, Kinerva identifies individuals based on their kinetic signature. Extracting gait and body characteristics from standard CCTV, it delivers court-ready, explainable evidence from crime scene footage.
- Identify suspects from low-quality, distant, or night-time video
- Match individuals even when faces are hidden or obscured
- Produce explainable, defensible results for court use
- Only one gait cycle may be enough for identification
- Build suspect shortlists and connect crime scenes from large video datasets
Why we are the leaders in Motion Analysis
Our Approach to Movement Analysis
Typical Approach of Others
Device-specific sensor data preprocessing to reconstruct original movement
Sensor Data PreProcessing
Standard, general purpose data smoothing methods
Delicate balance of bespoke features artfully emulating key elements of human motor programs
Feature Space
Standard physical features, or observable, domain-specific features
Proprietary AI toolchain designed for motion analysis
Machine Learning
Popular, general purpose Python AI libraries or statistics modules
Our Approach to Movement Analysis
Typical Approach of Others
Possibility to handpick features when only limited amount of data is available
Data Size
Deep Learning on large amount of data only
Explainable models built on meaningful features
Prediction Models
Unexplainable “Black Box” models
Cross-domain knowledge</strong> from having analyzed:
- handwriting
- cursor movement
- video-based fine and gross motor movements
- other time-series data
Millions of users served by our solutions across:
- user identification
- signature verification
- personality profiling
- assessing neurological conditions
Sensor Data PreProcessing
Our Approach to Movement Analysis
Device-specific sensor data preprocessing to reconstruct original movement
Typical Approach of Others
Standard, general purpose data smoothing methods
Feature Space
Our Approach to Movement Analysis
Delicate balance of bespoke features artfully emulating key elements of human motor programs
Typical Approach of Others
Standard physical features, or observable, domain-specific features
Machine Learning
Our Approach to Movement Analysis
Proprietary AI toolchain designed for motion analysis
Typical Approach of Others
Popular, general purpose Python AI libraries or statistics modules
Awards
Remote Monitoring Security Solution of the Year
Our partners
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