Current facial recognition technology is hampered by inaccuracy, false positives, lighting quality, facial viewpoint angles, skin tone, and ethnicity resulting in limited real-world utility.
The combination of dimensional facial data and Artificial Intelligence based deep learning facial recognition technology solves these challenges by merging data-rich image files with advanced deep-learning, true AI-based software to create the end-to-end identity management solution governments and private industry have been searching for.
An Overview of Biometric Capture and Rendering Platform
Within the NEJAVI ecosystem we now have a unified biometric capture and rendering platform for global government & corporate identity management, security, prisons/criminal justice, FinTech, medical/telemedicine, and AR/VR applications.
The solution creates and synthesizes true and accurate dimensional source data, including 360-degree imaging, precision 3D files, voice capture, biometric measurements, and scans & detection signals—which significantly improves the accuracy and performance of imaging-centric computer vision technologies, such as facial recognition, skin anomaly detection, and 3D file building.
This is the only imaging platform that can create virtual camera arrays replicating up to 360 individual cameras, along with integrated infrared depth scanning sensors for the creation of 3D imagery with unprecedented speed, flexibility, and accuracy.
“Images and associated metadata can be exported as individual stills, video, and 3D files to integrate with facial recognition and other computer vision systems.”
An Overview of AI Facial Recognition
The advanced “Artificial Intelligence” based facial recognition technology dramatically outperforms traditional facial recognition systems. The system utilizes its deep learning neural network to process and understand incoming information allowing it to achieve incredibly high accuracy rates in the most challenging surveillance situations.
Active Surveillance &
The facial recognition system is purpose built for wide area surveillance, identification, and access control scenarios requiring high accuracy in unconstrained real-world environments. Designed to be camera agnostic, the solution can be successfully deployed on existing IP-based CCTV surveillance networks without expensive hardware upgrades. Efficient operating architecture has been designed into the system from the beginning. Recognized by NVIDIA as a production-ready, field proven solution; the system is included within it’s deep learning Metropolis Software Partner Program. The advanced system architecture can process sixty (60) individual camera streams per server providing three times the efficiency of major competitors.
Overview of the combined Product
This innovative 360-degree dimensional imaging technology combined with advanced deep learning computer vision facial recognition software creates a complete, end-to-end solution for wide area surveillance, biometric identification, and movement registration. The technology provides superior recognition capability to conventional approaches.
By training the deep learning recognition software with the high-resolution dimensional facial data generated by the oVio360TM, the facial recognition system has the necessary source data to generate the most robust neural identification models. The next time known “watch-list” individuals are encountered by the surveillance network, recognition can be achieved from nearly all viewpoints where a portion of the face is visible. Identification is not impacted by skin tone or ethnicity and is more accurate under adverse lighting conditions, from overhead viewpoints, and when the face is obscured by sunglasses or partially concealed, compared to watch-list individuals enrolled with standard photography or stills extracted from video footage.
Contact NEJAVI to find out more.