GROW R304A DC4.2-6V 208*288 Pixel Fingerprint Module Scanner Reader Free SDK With 1500 Capacity For Arduino Windows Android
Price:
Negotiable
MOQ:
1
Delivery Time:
Peak Season Lead Time: within 15 workdays Off Season Lead Time: within 15 workdays
Product Description
Description
·Communication interface : USB and UART
·1:N Identification (One-to-Many)
·1:1 Verification (One-to-One)
·High speed fingerprint identification algorithm engine
·Self study function
·Fingerprint feature data read/write functions
·Get Feature Data of Captured fingerprint and Verify/Identify Downloaded Feature with Captured
·Fingerprint Identify Downloaded Feature with Captured fingerprint
·Security Level setting
·Able to set BaudRate/ Device ID/Device Password
·Operating system:Windows 98, Me, NT4.0, 2000, XP,WIN 7 or Android
·1:N Identification (One-to-Many)
·1:1 Verification (One-to-One)
·High speed fingerprint identification algorithm engine
·Self study function
·Fingerprint feature data read/write functions
·Get Feature Data of Captured fingerprint and Verify/Identify Downloaded Feature with Captured
·Fingerprint Identify Downloaded Feature with Captured fingerprint
·Security Level setting
·Able to set BaudRate/ Device ID/Device Password
·Operating system:Windows 98, Me, NT4.0, 2000, XP,WIN 7 or Android
Specifications
·Interface:USB 2.0 and UART(3.3V-TTL logic)
·Resolution:508 DPI
·Work Current: <55mA
·Voltage: DC 4.2-6.0V
·Resolution:508 DPI
·Work Current: <55mA
·Voltage: DC 4.2-6.0V
·Fingerprint capacity:1500
·Security Level: 1-5, default is 3
·Sensor Array: 208*288 pixel
·Security Level: 1-5, default is 3
·Sensor Array: 208*288 pixel
·Template Size: 512 bytes
·Fingerprint reader module size: 20.4 * 33.4 (mm)
·Effective collection area: 12*17.5 (mm)
·ScanningSpeed: < 0.2 second
·Verification Speed: < 0.3 second
·Matching Method: 1:1; 1:N
·FRR (False Rejection Ratio): ≤0.01%
·FAR (False Acceptance Ratio): ≤0.00001%
·Work environment: -20°C ---55°C
·Work Humidity: 20-80%
·Communications baud rate (UART): (9600 × N) bps where N = 1 ~ 12(default N = 6, ie 57600bps)
·Fingerprint reader module size: 20.4 * 33.4 (mm)
·Effective collection area: 12*17.5 (mm)
·ScanningSpeed: < 0.2 second
·Verification Speed: < 0.3 second
·Matching Method: 1:1; 1:N
·FRR (False Rejection Ratio): ≤0.01%
·FAR (False Acceptance Ratio): ≤0.00001%
·Work environment: -20°C ---55°C
·Work Humidity: 20-80%
·Communications baud rate (UART): (9600 × N) bps where N = 1 ~ 12(default N = 6, ie 57600bps)
Files
·All fingerprint module support with Arduino, Android, Windows, Linux, .Net and so on.·Provide Free SDK Files
·Provide User Manual
Principle and Implementation of Mobile Fingerprint Recognition
The premise of fingerprint recognition is to collect fingerprints. Currently, there are mainly two types of collection methods: sliding and pressing.
Step 1: Fingerprint Collection
Sliding collection is the process of sliding a finger over a sensor, allowing the phone to capture a fingerprint image of the finger. Sliding acquisition has the advantages of relatively low cost and the ability to capture large-area images. However, this collection method has the problem of poor user experience, as users need a continuous and standardized sliding motion to achieve successful collection, greatly increasing the probability of collection failure. A certain brand of mobile phone once used this collection method, which was criticized for the shortcomings of sliding collection.
As the name suggests, press based collection is the process of collecting fingerprint data by pressing on a sensor. While this method provides a better user experience, it is more expensive and technically challenging than sliding based collection. In addition, due to the smaller area of fingerprints collected at once compared to sliding collection, multiple collections are required to piece together larger fingerprint images. This must rely on advanced algorithms, using software algorithms to compensate for the relatively small fingerprint area obtained by sliding and pressing collection, in order to ensure the accuracy of recognition.
Step 2: Fingerprint evaluation
After collecting fingerprints, the quality of the collected fingerprints is evaluated. If they are not qualified, they need to be collected again. If they are qualified, the image will be enhanced and refined.
Step 3: Extract "features"
After processing, the binary image, refined image, and feature extraction image will be obtained in sequence. After obtaining a relatively clear image, feature extraction begins. After feature extraction and data storage, the next step of matching work can be carried out.
Step 4: Fingerprint matching
One thing to note in matching is that two sample images of the same finger may differ due to differences in finger displacement, deflection, and pressure. This requires calibration during matching, such as feature point set calibration, to ensure the accuracy of fingerprint recognition.
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Company
Hangzhou Grow Technology Co., Ltd.
Location
Room 310, Building 1-B, No. 279 Shiqiao Road, Xiacheng District, Hangzhou, Zhejiang, China 310012
Contact Person
Winni Wu