AnyVision created a world-leading face recognition algorithm capable of handling large number of faces in real life scenarios. Among these are the following scenarios: Airports – Extract, analyze and identify people in a crowded airport environment. Safe Cities – Track suspect’s route throughout multiple cameras in the city. Borders – Receive real-time alerts when known POI’s approach or try to cross a border.
Face Recognition In Motion
AnyVision sophisticated face recognition algorithms are able to work in the most challenging setups there are.
When taking into account today’s mobile way of life, AnyVision has created a unique solution to provide face recognition on the go, enabling mobile devices and mobile stations to be deployed on the ground in no time.
Face Recognition For Authentication
AnyVision has harnessed the impeccable accuracy level of it’s algorithms to create the world’s first true bank security level authentication software, using your face only. Our software is used in various use cases, such as:
Banks – Extract, analyze and store face images of all bank visitors and ATM users. Face permission – Generate rule based system notifications. Public space – Prevent entrance of unwanted individuals into sensitive locations.
AnyVision is the world’s leading Face Recognition company. Our mission statement is to ensure a better and safer daily life for everyone.
That mission has led us to create breakthrough technology – the first real-time human recognition system for surveillance cameras – with the help of our top academic researchers.
Unlike traditional face recognition or video analysis solutions, AnyVision’s technology utilizes deep-learning neural networks that imitatea human’s ability to recognize. This allows our technology to adapt and learn from new situations while being less computationally intensive than other solutions.
Imagine being able to obtain actionable intelligence (in real time) from the standard video surveillance footage recorded by the countless security cameras deployed worldwide. Now you can!
I believe that such a capability is priceless in today’s world, and invite you to learn more about us.
EYLON ETSHTEIN CEO & Co-founder
The Numbers make it tactical....
People process through monthly
True Monthly Positive
False Monthly Positive
Face Recognition 3D morphable model
In the face recognition society, pose-invariant face recognition is the most challenging scenario. The existing face recognition systems/methods normally assume the input face images are frontal and near-frontal. However, this is not true where side-on and other partial-face poses are presented to the camera in most real-world applications. Our work is of great interest in helping tackle this challenge.
Estimation In Multi Modal Video
We further fine-tune a regressor based on the learned deep classifier. Next we combine the two models (classification and regression) to estimate approximate regression confidence. We present state-of-the-art results in datasets that span the range of high-resolution human robot interaction (close up faces plus depth information) data to challenging low resolution outdoor surveillance data.
Anyvision’s technology utilizes
Deep-learning neural networks that imitate a human’s ability to recognize, instead of a computer
algorithm that is more computing intensive, and also unable to adapt or learn from new situations.
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