Description
1. Integrated image collecting and algorithm chip together, ALL-in-One
2. Fingerprint reader can conduct secondary development, can be embedded into a variety of end products
3. Users can conduct secondary development, can be embedded into a variety of end products, such as: access control, attendance, safety deposit box, car door locks
4. Low power consumption, low cost, small size, excellent performance
5. Professional optical technology, precise module manufacturing technics
6. Good image processing capabilities, can successfully capture image up to resolution 500 dpi
7. Have finger detection function.
8. R307S fingerprint capacity: 1000 Brief description of R307S fingerprint module:
1. Fingerprint capacity:
The storage capacity of R307S fingerprint module is 1000. 2. Description of interface:
R307-S fingerprint module has RS232 and USB2.0 at the same time.USB2.0 interface can connect to the computer;RS232 interface is TTL level, and the default baud rate is 57600, which can be changed. Please refer to the communication protocol.It can be connected with microcontroller, such as ARM, DSP and other equipment with serial port, and the microcontroller of 3.3v and 5V can be connected directly.When connecting to the computer, please pay attention to the level conversion, such as the MAX232 circuit.
3. Module detection
After the fingerprint module is powered on, the fingerprint collection window will flash, indicating that the self-test is normal. If it does not flash, please carefully check the power supply, whether it is connected backwards or wrongly.The chip has some heat when working normally, this is normal phenomenon, the product has passed strict test, can rest assured to use. Specifications
·Type: Optical
·Interface: USB/UART(TTL logical level)
·Resolution: 500 DPI
·Work Current: Typical ≤75 mA
·Voltage: DC 4.2-6.0V (or 3.3V)
·Fingerprint capacity: 1000
·Dimension: 52 *20*22 (mm)
·Image Capture Surface: 15*11 (mm)
·Backlight: Blue
·Sensor Life: 100 million times
·Static Indications: 15KV
·Character file Size: 256 bytes
·Template Size: 512 bytes
·Security Level: 5 (1,2,3,4,5(highest))
·Scanning Speed: < 0.3 second
·Verification Speed: < 0.2 second
·Matching Method: 1:N
·FRR (False Rejection Ratio): ≤0.1%
·FAR (False Acceptance Ratio): ≤0.0001%
·Operating Environment Temperature: -20°C ---50°C
·RS232 communications baud rate: 9600BPS~115200BPS changeable Files
·All fingerprint module support with Arduino, Android, Windows, Linux, .Net and so on.
·Provide Free SDK Files
·Provide User Manual
Progress in Biological Fingerprint Scanning Technology
Biometric fingerprint scanning technology, as an important branch of biometric recognition, has undergone significant innovation and progress in recent years. This technology is based on the uniqueness of individual fingerprints and provides strong support for fields such as identity verification, security monitoring, and medical identification through high-precision collection and analysis.
In the early days, fingerprint scanning mainly relied on traditional ink pressing methods, which were cumbersome and inefficient to operate. With the advancement of technology, the introduction of advanced acquisition technologies such as optics, capacitance, ultrasound, and biological radio frequency has greatly improved the accuracy and convenience of fingerprint recognition. Optical fingerprint scanning captures fingerprint images through the principle of light reflection, while capacitive technology utilizes the difference in capacitance between the finger and the sensor to form fingerprint images. The application of these technologies not only simplifies the collection process, but also significantly improves recognition accuracy.
Ultrasonic scanning technology is a major highlight in recent years. It measures the ultrasonic signals reflected from the surface of fingerprints to obtain more precise fingerprint depth information, effectively resisting external factors such as dust and oil interference. Biological radio frequency technology goes further by using radio frequency signals to penetrate the dermis layer of fingers and capture internal features of fingerprints, adding a new security dimension to fingerprint recognition.
At the algorithmic level, the integration of advanced technologies such as machine learning and deep learning enables fingerprint recognition systems to process complex fingerprint images more intelligently, improving recognition speed and accuracy. These technologies, through continuous learning and optimization, can more accurately identify subtle fingerprint features and achieve efficient verification even under low-quality fingerprint conditions.
The application of fingerprint recognition technology is particularly widespread in fields such as financial payments and security monitoring. In the field of smartphones, the emergence of under screen fingerprint technology not only increases the screen to body ratio of devices, but also provides users with a more seamless identity verification experience. In the payment scenario, fingerprint recognition has become a standard feature for many mobile payment platforms and online banks as a fast and secure means of identity verification. At the same time, the application in access control systems, attendance management, and monitoring systems further strengthens security protection and management efficiency.
However, fingerprint recognition technology also faces some challenges. Environmental factors such as dust, sweat, and scars may affect the quality of fingerprints, leading to a decrease in recognition accuracy. In addition, the threat of fingerprint forgery cannot be ignored. Although modern technology can effectively resist most forgery methods, continuous technological innovation is still the key to ensuring security.
To address these challenges, researchers are working on developing higher precision collection technologies, such as using high-resolution sensors to capture subcutaneous fingerprint structure information and combining artificial intelligence algorithms to enhance anti-counterfeiting capabilities. At the same time, the integration of multimodal biometric technology, such as combining fingerprint recognition with facial recognition, iris recognition, etc., has also become an important direction for improving security.
In summary, biological fingerprint scanning technology has made significant progress in recent years, not only achieving a qualitative leap in collection efficiency and recognition accuracy, but also playing an irreplaceable role in multiple key fields. With the continuous evolution and innovation of technology, fingerprint recognition technology will continue to deepen its application in security verification, identity management, and other aspects, contributing to the construction of a more intelligent and secure social environment.
