Description
AI face recognition camera, liveness recognition + cat eye vision, financial grade liveness detection algorithm/support photo distribution function
Supports live detection and resists various attacks. It uses multi-modal live anti-counterfeiting algorithms to effectively block attacks from photos, videos, and various head models and dummies. Under the premise of a 99% pass rate, a false recognition rate of less than one in a million can be achieved
An integrated module integrating deep learning dynamic visible light face recognition algorithm, based on financial-grade binocular 3D liveness detection algorithm, equipped with binocular cameras to complete face liveness detection, face capture, feature extraction/comparison and user information storage and other functions.
It has the characteristics of small area, low power consumption and fast recognition. The module not only enhances the adaptability to ambient light and enhances anti-counterfeiting capabilities, but also improves the usability for people of different heights, expands the recognition angle, and greatly improves the user-friendly experience
It can be widely used in facial recognition scenarios such as smart door locks, smart access control systems, and time and attendance machines, 3D face payment systems, smart device unlocking and human-computer interaction applications, and low-power battery face recognition systems
Name: Face recognition module
Communication interface: UART&USB
Face algorithm: supports deep learning visible light face recognition algorithm
Power supply requirements: DC5.5V~9V@1A
Working temperature: -20℃~+60℃
Storage temperature: -30℃~+70℃
Environmental humidity: 10% ~ 93% without condensation
Applicable scenarios: smart home, security monitoring, smart door locks, mobile vending machines and other fields
Colour:Black
Material:PCB
Package Contents:
1x Face Recognition Module
1x 1.25*4PIN terminal cable
Only the above package content, other products are not included.
Note: Light reflection and different displays may cause the color of the item in the picture a little different from the real thing. The measurement allowed error is +/- 1-3cm.
Supports live detection and resists various attacks. It uses multi-modal live anti-counterfeiting algorithms to effectively block attacks from photos, videos, and various head models and dummies. Under the premise of a 99% pass rate, a false recognition rate of less than one in a million can be achieved
An integrated module integrating deep learning dynamic visible light face recognition algorithm, based on financial-grade binocular 3D liveness detection algorithm, equipped with binocular cameras to complete face liveness detection, face capture, feature extraction/comparison and user information storage and other functions.
It has the characteristics of small area, low power consumption and fast recognition. The module not only enhances the adaptability to ambient light and enhances anti-counterfeiting capabilities, but also improves the usability for people of different heights, expands the recognition angle, and greatly improves the user-friendly experience
It can be widely used in facial recognition scenarios such as smart door locks, smart access control systems, and time and attendance machines, 3D face payment systems, smart device unlocking and human-computer interaction applications, and low-power battery face recognition systems
Name: Face recognition module
Communication interface: UART&USB
Face algorithm: supports deep learning visible light face recognition algorithm
Power supply requirements: DC5.5V~9V@1A
Working temperature: -20℃~+60℃
Storage temperature: -30℃~+70℃
Environmental humidity: 10% ~ 93% without condensation
Applicable scenarios: smart home, security monitoring, smart door locks, mobile vending machines and other fields
Colour:Black
Material:PCB
Package Contents:
1x Face Recognition Module
1x 1.25*4PIN terminal cable
Only the above package content, other products are not included.
Note: Light reflection and different displays may cause the color of the item in the picture a little different from the real thing. The measurement allowed error is +/- 1-3cm.
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