Fingerprint recognition principle

Fingerprint recognition principle
The capacitive sensor determines which position is the ridge and which position is the valley according to the different capacitance values ​​formed by the ridges and valleys of the fingerprint and the semiconductor capacitive sensing particles. Its working process is to pre-charge the capacitive sensing particles on each pixel to a certain reference voltage. When the finger touches the surface of the semiconductor capacitive fingerprint, because the ridges are convex and the valleys are concave, different capacitance values ​​will be formed at the ridges and valleys according to the relationship between the capacitance value and the distance. Then the discharge current is used for discharge. Because the capacitance values ​​corresponding to the ridges and valleys are different, the discharge speed is also different. The pixels under the ridges (high capacitance) discharge slowly, while the pixels under the valleys (low capacitance) discharge quickly. According to the different discharge rates, the positions of the ridges and valleys can be detected, thereby forming fingerprint image data.

2 Introduction to FPS110
The FPS110 fingerprint sensor chip has the advantages of small size, low cost, low power consumption and high security. It can be widely used in any field that requires security authentication, such as banks, computer networks, fingerprint access control, fingerprint attendance and many other aspects. This will undoubtedly replace the original identification technology and become the latest development trend of identification technology application in the 21st century. The integrated A/D converter can establish a digital interface with EPP, USB or MCU, so that this device can be easily applied to any identification application system. The scanned image is 8×280=22.4 million pixels. Up to 2 MH, equivalent to 1780 frames per second, 1 million fingers can read fingerprints without trouble, low power consumption, 4.5 mA during image acquisition, 1.5 mA during navigation, and less than 10 uA in sleep mode. The pin functions are shown in Table 1.

3 System Hardware Design
3.1 Power
Supply The power supply circuit supplies power to the entire system, and the power supply has a voltage conversion chip that can convert 5 V to 3.3 V and 3.3 V to 1.8 V. It meets the system requirements of FPS110 input voltage 3 V to 5.5 V and MCU processing chip 3.3 V voltage power supply.
3.2 Fingerprint Collection
Each column of the FPS110 sensor has two sample-and-hold circuits, one for storing the voltage across the capacitor before discharge and the other for storing the voltage across the capacitor after discharge. The difference between the two sample-and-hold circuits can measure the change in capacitance. The sensitivity of the sensor can be corrected by adjusting the discharge time and discharge current, and the modification of the discharge time and discharge current can be performed by reading and writing the discharge current register (DCR) and discharge time register (DTR) inside the sensor.
3.3 MCU Microprocessor
The MCU product MSP430F1002 from NI is used, which has up to 8 kB of reprogrammable flash memory, 256 bytes of read-only memory (RAM), multi-channel pulse width modulation (PWM) timer, watchdog timer and on-board battery exhaustion protection. The high-speed universal synchronous asynchronous receiver and transmitter (USART) integrated in the MSP430F12x2 device can be set to any mode for UART or serial peripheral interface (SPI). The MCU and JTAG emulator are connected with a JTAG line, and then the software monitor is used to process the signal sent by FPS110 through programming.
3.4 Serial communication
The USB communication interface card uses the FT8U245BM chip from FTDI to connect FPS110 and MCU. Its main function is to send the control commands and data sent by FPS110 to the main MCU of the test platform, so that the test platform can complete various test tasks; on the other hand, it is to send the self-test information and test results of the multi-MCU system in the test platform to FPS110. For FPS110 to judge and analyze.
The hardware block diagram of the whole system is shown in Figure 1.

Because the image itself has a huge storage capacity, the system needs an external memory to ensure that there is enough storage space to store the image on a USB flash drive. At the same time, the system can also be connected to the network through a data cable to achieve remote control functions.
When no one touches the screen, the MCU is placed in power-saving sleep mode. Once the user has input, the system exits the low-power state and enters the normal execution state 4. The control software determines the user's needs and executes the corresponding functions.

5 Experimental simulation
The data simulation was performed using the MATLAB7.0 image processing box of The MathWoks Company. The computer configuration CPU was 2.06 GZH from INTEL Company and the memory was 1 GZH from Kingston Company. The average simulation time for one image data was 0.73 seconds. The recognition rate error was less than 1/310 000, which was completely in line with the requirements. Figure 3 is a random thumb fingerprint image, and Figure 4 is the corresponding image collected from the fingerprint library.

6 Conclusion
Using the FPS110 fingerprint collector to collect data, when the user's fingerprint does not match the preset fingerprint in the fingerprint library, the user is refused to use it. It can be used to identify the user's identity or replace manual password entry, and has a wide range of application prospects.