Face Recognition Application for Security Purposes
POSTED ON
August 12, 2015
POSTED BY
Aayan Arif
POSTED ON August 12, 2015
POSTED BY Aayan Arif
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The Robotics Division at vteams has developed a real-time Automatic Face Recognition Application using core Python, LBPH algorithm and OpenCV 2.4.10 library.
The Robotics Division at vteams has developed a real-time Automatic Face Recognition Application using core Python, LBPH algorithm and OpenCV 2.4.10 library. Facial recognition being the most convenient biometric technology, works with the most apparent individual identifier – the human face.
In an experiment, we used Face Recognition System to monitor employee attendance. We programed this application to automatically detect and recognize when particular employees enter or leave the office premises.
Face Recognition Application takes into account characteristics of a person’s facial features fed by a high resolution digital video camera. The application then sends a detected face image to LBPH (Local Binary Patters Histogram) recognition algorithm that starts processing it. It measures the overall features of the face, including distances between nose, eyes, jaw edges, and mouth. These measurements are then stored in a database and used for evaluation when a person stands before the camera.
With every human face having almost 80 nodal points, each face is different from each other by certain landmarks that make up the facial features. Following nodal points are used by our application to identify a human face:
Distance between the eyes
Width of the nose
Depth of the eye sockets
The shape of the cheekbones
The length of the jaw line .
The following four-stage process explains the way our Face Recognition Application operates:
Capture – a physical or behavioral sample is captured by the system during registration
Extraction – a template is created from the unique data extracted
Comparison – a new sample is then compared with the template
Matching – the system then decides if the features extracted from the new sample are matching with the training samples or not .
A Face Recognition Application for High Traffic Areas:
Our application is also perfect for areas such as:
Government offices
Banks
Airports and railway stations
Public transportation
Business of all kinds
Corporate sector .
On average, our system makes the decision within 5 seconds, which is pretty good when installed at security checkpoints where the traffic is controlled and guided through a set path.
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ABOUT THE AUTHOR
Aayan Arif
Content Strategist at vteams - Aayan has over 8 years of experience of working with multiple industries.
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