IntellQ VIS Datasheet
While detecting faces those are immediately compared with a list of targeted and tracked individuals from a target database. When the system identifies a face of interest (target), it issues a warning so that appropriate measures can be taken quickly in order to reduce the risk of public and national security threats and to take the security and business procedures defined by the Security staff and/or Law Enforcement Agencies.
Independent testing by our users and other relevant FR authorities confirms that IntellQ VIS Artificial Intelligence (AI) system technology with applied advanced Machine Learning algorithms implemented inside IntellQ VIS Machine Learning Engine provide the fastest and most accurate syntax and facial recognition verification and is most resistant to age variation, race variation, angle of view, and person’s face coverage.
IntellQ VIS Artificial Intelligence (AI) with Machine Learning Engine and Deep Learning capabilities automatically adjusts and enhances the speed and accuracy of face recognition IntellQ VIS system regardless of changes in face appearance, changes in age, accessories like glasses, caps, cosmetic makeup or beard and moustache and growing facial hair.
IntellQ VIS Machine Learning Engine uses highly complex computations to achieve the best possible match of all face(s) (enrolled under a “Target”) against data defined in “Media Sets”.
Unique FR database of all user targets and other FR entities
Unique database of all collected/received images and videos that are subject to FR processing
Flexibility of multi-integration:
High-performance INTELLQ FR system compatibility with multiple photo and video sources (video cameras, cameras, mobile devices, photo and video content capture devices, etc.)
Advanced and flexible integration interfaces to strategic and tactical systems and units that collect and contain photo and video documents for FR processing
Flexible Integration Interfaces to Open Sources (Internet sources such as social networks, forums, blogs, deep web, etc.)
Flexible Integration Interfaces to database and file systems that contain FR targets and other FR entities
Artificial Intelligence algorithms for Biometric Detection, Identification and facial recognition which can uniquely identify a person’s face by analysing patterns based on the texture and shape of a person’s face
Discovering people of interest (targets and other FR entities) in monitored areas in real-time (e.g. suitable for detecting unwanted people in relation to expected, security staff and VIP persons)
Real-time Alerting System and actions (pre-defined or ad-hoc defined) that need to be undertaken (early proactive e-mail, SMS or popup window alerts)
GIS System (FR Geographic Information Subsystem) for visualization and geo-positioning on the map (geo-location of IP video cameras, photo cameras, mobile phones, targeted people, etc.)
SNA subsystem (Social Network link Analysis subsystem – SNA) for visualization of complex social relationships defined by FR system and other SNA parameters
Analytical system for advanced analysis and reporting
Profiling system for creating profiles of defined targets and FR entities
Project Management system for defining and producing complex FR cases
Project Collaboration System (FR Communication Subsystem)
User rights management system for defining the user rights and roles
INTELLQ FR Server component with Integrated FR Module for Detection, Identification and Facial recognition
INTELLQ FR Client component for real-time operation
INTELLQ FR Client component for non-real-time operation
INTELLQ FR Mobile component for real-time operation