FACEHAWK processing engine is deep/ machine learning (DL/ ML) based, which does face finding (capture faces), face assessment (assess the quality of an image) and face encoding & matching through the neural network. The FACEHAWK is designed in a way, which is compatible with both centralized and distributed deployment environment. By ensuring information security aspect, FACEHAWK has a CIA Triad layer (Confidentiality, Integrity and Availability). It is well equipped with internal features to kill malicious thread and generate timely alerts. A cybersecurity layer can scan all traffic packet by packet and scrutinize any ill will of the sender and receiver.
The design architecture of FACEHAWK based on micro-service architecture (MSA), which is state-of-the-art, offers a variety of features. It guesses moods and gestures along with the trend, which will help the end-user to know the gesture estimates to develop preventive actions. Trends will make end-user capable of decision making. A live monitoring and alert system will support the end-user to handle any immediate incidental situation.
Distinguishing the effectiveness of FACEHAWK, it is imperative to state that it is a highly scalable application to convert its dimension according to business strategic need. It offers a highly economical deployment solution to support customer economics.
CAPTURE
Obtains fascial images from video streams [CCTV surveillance cameras, mobile video cameras or archived video footage), still images storage (mobile cameras,smart devices and digital databases) and third party integrated system
ASSESS
Assess individual frames of video and still images, detects faces and analyzes each face to determine its unique facial signature to create a small template for each unique face
MONITOR
Compares each template image against an enrolled image database until a match is found while maintaining a history of matches
REACT
Allows configuration of real-time alerts or messages to be sent to users or external integrated systems if there is a positive match against a database image along with offering a suite of post-event image comparison tools
Once the camera detects/ capture a face, a face recognition system determines the head’s position, size, pose, and unique characteristics. Every face has numerous, distinguishable landmarks — the different peaks and valleys that make up facial features. These landmarks are called nodal points. Each human face has approximately 80 nodal points. Some of the nodal points measured by the nodal points. Some of the nodal points measured by the
o Width of the nose;
o Depth of the eye sockets;
o The shape of the cheekbones;
o The length of the jaw lines.
The system translates nodal-point measurements into a numerical code or set of numbers, called a faceprint, representing the features on a subject’s face that can be compared to faces in the database. A match is then verified from the faceprint.
USER MANAGEMENT
DETECTION
DECISION SUPPORT ANALYTICS
NOTIFICATIONS
REPORTING
The matching speed of FACEHAWK is 150 milliseconds with a 98% recognition rate. It constantly performs its function on 25fps with an error rate of 0.2%. It has a user-friendly Graphic User Interface (GUI), which empowers its user to use the software efficiently. It also empowers its users in decision making and to take preventive actions based on the in-built business intelligence reports generated through live events. It is highly secure as well as cost-effective.
FACEHAWK act as a manufacturer of the software with all the intellectual property rights of FACEHAWK.
Platinum Partners - Complimentary capability, Scale for operational size or geographical coverage and vertical integration along the industry value chain. Moreover, we call them distributor reseller and system integration.
It comprises of two types of partners i.e., GOLD and Silver. They are responsible for the awareness and credibility building, prospective/ lead generation, Sales, and Delivery/ Implementation.
