Grow R304A DC4.2-6V 208*288 Pixel Capacitive Fingerprint Module Scanner
Certification:CE
Minimum Order Quantity:1
Delivery Time:Peak Season Lead Time: within 15 workdays Off Season Lead Time: within 15 workdays
Payment Terms:T/T, PayPal
Place of Origin:China
Supply Ability:5000
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Verified Supplier
Location:
Hangzhou Zhejiang China
Address:
Room 310, Building 1-B, No. 279 Shiqiao Road, Xiacheng District, Hangzhou, Zhejiang, China 310012
Supplier`s last login times:
within 28 hours
Product Details
Company Profile
Product Details
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.
Grow R304A DC4.2-6V 208*288 Pixel Capacitive Fingerprint Module Scanner
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