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Face Recognition Camera Module

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Shenzhen Jupin Technology Co., Ltd.

City:shenzhen

Country/Region:china

Contact Person:MrLiao Shaoxiong

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China Binocular Face Recognition 3D Stereo Vision Camera Module wholesale

Binocular Face Recognition 3D Stereo Vision Camera Module

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Video Channel

Storage :8GB/16GB eMMC

Power :5V/1A

Output Format :Camera（IR）: RAW Camera（RGB）: RAW

Recommended Database :10,000

Module Size :84.0mm × 22.45mm × 19.35mm

Processor :Quad-core ARM Cortex-A7 32-bit, 1.5GHz, with integrated NEON and FPU Each core has a 32KB I-cache and 32KB D-cache, plus 512KB shared L2 cache Based on RISC-V MCU

Interface :Camera（IR）: MIPI Camera（RGB）: MIPI

Maximum Database :100,000

Recommended Face Recognition Angles :Yaw: ≤ ±30° Pitch: ≤ ±30° Roll: ≤ ±30°

Face Comparison :Feature Extraction Time: ~25 ms Single Comparison Time: ~0.0115 ms

Video decoding :4KH.264/H.26530fps 3840x2160@30encoding+3840x2160@30fpsdecoding

Image Sensors :Camera（IR）: GC2053 Camera（RGB）: GC2093

Pixel Size :Camera（IR）: 2.8 μm Camera（RGB）: 2.8 μm

Recommended Image :720P

Video encoding :4KH.264/H.26530fps 3840x2160@30fps+720p@30fpsencoding

Sensor Size :Camera（IR）: 1 / 2.9 Camera（RGB）: 1 / 2.9

System support :Linux

Operating humidity :10%～90%

Resolution :Camera（IR）: Center 800 Edge 600 Camera（RGB）: Center 800 Edge 600

Face Recognition Accuracy :Standard Testing Environment, 10,000-person Database: Without Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 99% With Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 95%

Enclosure Design :Aluminum alloy material with serrated heat sink back cover for efficient cooling

Lens :Camera（IR）: 4P Camera（RGB）: 4P

Liveness Detection :Monocular Liveness Detection Time: ~45 ms Binocular Liveness Detection Time: ~15 ms

NPU :Up to 2.0 Tops performance, supports INT8/INT16, strong network model compatibility, RKNN model conversion tool available for converting common AI framework models (e.g., Caffe, Darknet, MXNet, ONNX, PyTorch, TensorFlow, TFLite) and algorithm support

Face Detection :Face Detection Time: ~23 ms Face Tracking Time: ~7 ms

Memory :1GB/2GBDDR4

Filter Wavelength :Camera（IR）: 850 nm Camera（RGB）: 650 nm

Payment Terms :T/T

Optical Distortion :Camera（IR）: ≤0.5% Camera（RGB）: ≤0.5%

Focal Length :Camera（IR）: F2.0/4.3mm Camera（RGB）: F2.0/4.3mm

Host computer chip :RV1126

Focusing Distance :Camera（IR）: 80 cm Camera（RGB）: 80 cm

Model Number :JP1126

Place of Origin :China

MOQ :Negotiable

Price :Negotiable

Supply Ability :200+/day

Delivery Time :5-8 work days

Operating temperature :-10℃～60℃

Power Consumption :Typical Power Consumption: 2.8W (5V, 560mA) Maximum Power Consumption: 4.3W (5V, 860mA) Minimum Power Consumption: 0.71W (5V, 142mA) Power Supply Recommendation: 5V/1.2A or higher

Field of View :Camera（IR）: D70°H62°V38° Camera（RGB）: D70°H62°V38°

Minimum Face Size for Recognition :Without Liveness Detection: 50 x 50 pixels With Liveness Detection: 90 x 90 pixels）

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JP1126 Intelligent Dual-Lens Camera Module Up to 2.0 Tops performance, supports INT8/INT16 5V/1A

JP1126 Intelligent Dual-Lens Camera Module Features:

High-performance AI intelligent vision processor
Powerful AI computing capabilities
Wide dynamic range dual-lens camera
AI vision system with integrated master-slave functionality
Robust video encoding and decoding capabilities
Compact design
Open-source documentation
Extensive application scenarios
Equipped with a 2MP RGB+IR infrared dual-lens camera module, supporting liveness detection to effectively prevent spoofing using photos, videos, or wax figures.
Ensures accurate facial recognition even in complex and extreme lighting conditions.
Widely applicable to facial recognition, gesture recognition, access control systems, smart finance, smart construction sites, smart transportation, and more.By adopting this camera module solution, you can achieve fast and low-barrier implementation of facial recognition terminal products.

JP1126 Intelligent Dual-Lens Camera Module Parameter:

Processor:	Quad-core ARM Cortex-A7 32-bit, 1.5GHz, with integrated NEON and FPU Each core has a 32KB I-cache and 32KB D-cache, plus 512KB shared L2 cache Based on RISC-V MCU
NPU:	Up to 2.0 Tops performance, supports INT8/INT16, strong network model compatibility, RKNN model conversion tool available for converting common AI framework models (e.g., Caffe, Darknet, MXNet, ONNX, PyTorch, TensorFlow, TFLite) and algorithm support
Memory:	1GB/2GBDDR4
Storage:	8GB/16GB eMMC
Video encoding:	4KH.264/H.26530fps 3840x2160@30fps+720p@30fpsencoding
Video Decoding:	4KH.264/H.26530fps 3840x2160@30encoding+3840x2160@30fpsdecoding
System support:	Linux
Power:	5V/1A
Image Sensors:	GC2053 GC2093
Module Board Dimensions:	80* 16* 17.6mm (L* W* H)
Resolution:	1920*1080
Pixel Size:	2.8 μm
Interface:	MIPI
Focal Length:	F2.0/4.3mm
Maximum Database:	100,000
Face Recognition Accuracy:	Standard Testing Environment, 10,000-person Database: Without Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 99% With Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 95%

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