Offline AI Binocular 3D Vision Recognition Sensor (Face / Palm Vein / QR Code)

SKU: SEN0677 Brand: DFRobot
Add robust biometric security to project with this AI vision module. Features offline face, palm vein & QR code recognition with 3D liveness detection....
$96.70 AUD, inc GST
$87.91 AUD, exc GST

Available with a lead time
Expect dispatch between Oct 17 and Oct 21

- +

0 from local stock, 1 supplier stock; your order will dispatch between Dec 1 to Dec 10. And yes, stock levels and lead times are accurate!

Shipping:

  • $7+ Standard (5+ days*, tracked)
  • $11+ Express (2+ days*, tracked)
  • FREE Pickup (Newcastle only - must order online*)

Shipping costs may increase for heavy products or large orders.

Exact shipping can be calculated on the view cart page.

*Conditions apply, see shipping tab below.

This AI binocular vision sensor is a standalone, multi-modal recognition module engineered for high-security and versatile identity authentication. It operates on a powerful onboard AI processor, utilizing dual color and infrared cameras to perform all computations locally. The core of its functionality lies in its three-in-one identification capability, integrating robust Face Recognition, secure Palm Vein Recognition, and universal QR Code scanning into a single, compact device. Key features include advanced 3D Liveness Detection for anti-spoofing, complete offline AI processing that eliminates any computational load on the host system, and local storage for up to 1,000 users. With its simple UART interface, this offline AI vision sensor is ideal for rapid integration into applications such as secure access control, smart home systems, and automated retail terminals.

AI Binocular Vision Sensor detect Face & Palm Vein


High-Security Biometrics with 3D Liveness Detection

Security is paramount in authentication systems. This face recognition sensor employs a binocular camera system (RGB + Infrared) to capture 3D depth information, enabling a sophisticated liveness detection algorithm. This technology effectively distinguishes a live person from a 2D representation, providing robust protection against spoofing attempts using photos or videos. With a Face Recognition False Acceptance Rate (FAR) as low as 0.001%, the module delivers a level of security suitable for critical access control applications.


Versatile 3-in-1 Recognition Capability

The AI binocular vision sensor offers unparalleled flexibility by combining three distinct recognition methods. The deep learning-based face recognition provides fast, contactless access. For enhanced security, the palm vein and palm print recognition algorithm identifies the unique subcutaneous vein patterns, a biometric marker that is extremely difficult to replicate. Finally, the integrated QR code decoding offers a convenient solution for granting temporary access, processing payments, or device pairing, making the sensor adaptable to a wide range of operational requirements.


Powerful On-Module Offline AI Processing

Equipped with a dedicated System-on-Chip (SoC) featuring a 0.5 TOPS Neural Processing Unit (NPU), the AI vision camera handles all complex AI algorithms internally. This "edge computing" design means it requires zero computational resources from the host device, allowing it to be controlled by any microcontroller, including basic models like an Arduino Uno. All user data, including 1,000 face and 1,000 palm vein templates, is stored and processed locally, ensuring system performance, data privacy, and functionality even without a network connection.


Seamless Integration via UART & USB

Designed for ease of implementation, the binocular camera communicates recognition results through a simple UART serial protocol, making it straightforward to integrate into both new and existing product designs. Furthermore, the USB interface supports the UVC (USB Video Class) protocol, allowing the module to stream video like a standard webcam. This dual functionality is invaluable for applications requiring video feeds for monitoring, visual confirmation, or building video intercom systems.

AI binocular vision recognition sensor hardware composition diagram

Figure: AI binocular vision recognition sensor hardware composition diagram

Specification

Hardware Parameters

  • SoC: Arm CPU@900MHz, [email protected], RISC-V@600MHz
  • DDR2: 64MB
  • Flash: 32MB
  • Cameras: Dual 1/5” CMOS, 2MP, dual MIPI interfaces
  • Lens: FOV: 83° diagonal; optimal focus: 60cm
  • LED: IR@850nm, RGB@650nm (90° illumination)
  • USB: UVC video transmission (expandable to UAC); MJPEG output (H.264/YUY2 expandable)
  • Communication: UART@115200 baud
  • Power Parameters

  • Supply voltage: 5-12V
  • Operating current: 320-330mA@8V
  • Standby current: 120–130mA (auto-detection mode)
  • Shutdown current: 0µA (non-auto-detection mode)
  • Other Parameters

  • Operating temperature: -20°C to +60°C
  • Storage temperature: -30°C to +70°C
  • Humidity: 10–93% RH (non-condensing)
  • Audio I/O: Expandable (speaker/microphone)
  • Boot time: 900ms–2.5s (varies with stored user count)
  • User capacity: 1,000 faces; 1,000 palm veins
  • Algorithms: Binocular liveness detection, deep learning face recognition, palm vein recognition, QR code recognition
  • Liveness metrics:
  • FAR (False Acceptance Rate) =1%
  • FRR (False Rejection Rate) =1%
  • Face recognition:
  • Pass rate: 98.85%
  • FAR: 0.001%
  • Recognition angle: ±20° pitch/yaw; supports multi-angle enrollment
  • Detection range: Face: 30–120 cm, palm vein / QR code: 15cm
  • Dimensions: 57.8×20×10.12mm
  • Documents

  • Product wiki
  • Development Manual
  • This product is listed in:

    Home Lab>Machine Learning
    Sensors>Camera

    Product Comments

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    Our physical address (here's a PDF which includes other key business details):

    40 Aruma Place
    Cardiff
    NSW, 2285
    Australia

    Take a look at our customer service page if you have other questions such as "do we do purchase orders" (yes!) or "are prices GST inclusive" (yes they are!). We're here to help - get in touch with us to talk shop.

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