payloads camera module core, China payloads camera module core products, payloads camera module core Manufacturers, China payloads camera module core catalog

{"cid":"91208930","title":"JP1126 Binocular Face Recognition 3D Stereo Vision Camera Module with Algorithm","imgUrl":"https:\/\/img.chinax.com\/nimg\/be\/e2\/2c44b86c388aa4f70389d49f674c-200x200-1.jpg","attrs":{"Price":"Negotiable","Brand Name":"","Model Number":"JP1126","Minimum Order Quantity":"Negotiable","Delivery Time":"5-8 work days","Video decoding":"4KH.264\/H.26530fps 3840x2160@30encoding+3840x2160@30fpsdecoding","Image Sensors":"Camera\uff08IR\uff09: GC2053 Camera\uff08RGB\uff09: GC2093","Pixel Size":"Camera\uff08IR\uff09: 2.8 \u03bcm Camera\uff08RGB\uff09: 2.8 \u03bcm","Recommended Image":"720P","Face Comparison":"Feature Extraction Time: ~25 ms Single Comparison Time: ~0.0115 ms","Lens":"Camera\uff08IR\uff09: 4P Camera\uff08RGB\uff09: 4P","Liveness Detection":"Monocular Liveness Detection Time: ~45 ms Binocular Liveness Detection Time: ~15 ms","Payment Terms":"T\/T","Host computer chip":"RV1126","Focusing Distance":"Camera\uff08IR\uff09: 80 cm Camera\uff08RGB\uff09: 80 cm","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\uff08IR\uff09: MIPI Camera\uff08RGB\uff09: MIPI","Maximum Database":"100,000","Recommended Face Recognition Angles":"Yaw: \u2264 \u00b130\u00b0 Pitch: \u2264 \u00b130\u00b0 Roll: \u2264 \u00b130\u00b0","Resolution":"Camera\uff08IR\uff09: Center 800 Edge 600 Camera\uff08RGB\uff09: 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","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","Operating temperature":"-10\u2103\uff5e60\u2103","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","Storage":"8GB\/16GB eMMC","Power":"5V\/1A","Output Format":"Camera\uff08IR\uff09: RAW Camera\uff08RGB\uff09: RAW","Recommended Database":"10,000","Module Size":"84.0mm \u00d7 22.45mm \u00d7 19.35mm","Video encoding":"4KH.264\/H.26530fps 3840x2160@30fps+720p@30fpsencoding","Sensor Size":"Camera\uff08IR\uff09: 1 \/ 2.9 Camera\uff08RGB\uff09: 1 \/ 2.9","System support":"Linux","Operating humidity":"10%\uff5e90%","Field of View":"Camera\uff08IR\uff09: D70\u00b0H62\u00b0V38\u00b0 Camera\uff08RGB\uff09: D70\u00b0H62\u00b0V38\u00b0","Minimum Face Size for Recognition":"Without Liveness Detection: 50 x 50 pixels With Liveness Detection: 90 x 90 pixels\uff09","Optical Distortion":"Camera\uff08IR\uff09: \u22640.5% Camera\uff08RGB\uff09: \u22640.5%","Focal Length":"Camera\uff08IR\uff09: F2.0\/4.3mm Camera\uff08RGB\uff09: F2.0\/4.3mm","Memory":"1GB\/2GBDDR4","Filter Wavelength":"Camera\uff08IR\uff09: 850 nm Camera\uff08RGB\uff09: 650 nm","Place of Origin":"China","Supply Ability":"200+\/day","Packaging Details":""}}