**Introduction**
Fingerprint recognition is a biometric identification technique that relies on the uniqueness and stability of an individual's fingerprint pattern. As society continues to advance, embedded fingerprint recognition technology has gained significant popularity in the market and has become a major focus of research and development in recent years. However, many existing embedded fingerprint algorithms still face challenges in terms of real-time performance and accuracy, indicating the need for further optimization to achieve more precise and efficient recognition.
This study presents the design and implementation of a fingerprint recognition system based on the STM32 microcontroller. The system collects fingerprint data using a fingerprint sensor, processes the data using a fingerprint algorithm, and integrates a human-computer interaction interface built on the VC++ platform to display the fingerprint image data. This approach ensures both functionality and user-friendly interaction.
**1. System Hardware Design**
**1.1 Structure Composition and Features**
The system uses the STM32F-103ZET6, a 32-bit ARM Cortex-M3 processor, as the main controller. The chip employs a Harvard architecture, integrating 64KB of RAM and 512KB of FLASH memory. It offers fast processing speed, compact size, and low power consumption, making it highly suitable for embedded image processing applications. The overall structure and functional block diagram of the fingerprint identification system are shown in Figure 1.
*Figure 1: System hardware block diagram*
The system hardware consists of several key modules: a fingerprint acquisition module, an SPI interface module, a fingerprint data storage module (SRAM), a program storage module (FLASH), a UART communication module, a fingerprint image processing module, and a result display module.
The working process begins with the system receiving a 5V regulated power supply through USB, which is then converted to 3.3V internally. Upon startup, the STM32 initializes the sensor registers, and the FPS200 fingerprint sensor collects images via the SPI interface. The STM32 communicates with the sensor, sends the collected data to its internal memory, and performs preprocessing, feature extraction, and matching. The final recognition result is displayed on the screen. Additionally, the STM32 transmits the fingerprint image data to a PC through asynchronous serial communication for visualization. Since a single image requires 76.8 KB of memory, the system expands SRAM to store the data. The system program is stored in FLASH, along with the fingerprint template, allowing for dynamic updates. The JTAG interface is used for simulation and debugging, with IAR for ARM used for program development.
**1.2 Fingerprint Acquisition Circuit Design**
Accurate fingerprint image acquisition is crucial for the system’s recognition performance. High-quality fingerprint images reduce the complexity of subsequent algorithms and enhance the overall recognition capability.
The FPS200 fingerprint sensor from Veridicom features a 500dpi resolution, a 300×256 sensor array, 256 grayscale levels, and 8-bit pixel data. It supports multiple interfaces, including MCU, SPI, and USB. For this design, the SPI interface is used due to its simplicity. The sensor operates at 3.3V, and the hardware circuit for fingerprint acquisition is shown in Figure 2.
*Figure 2: Schematic diagram of the fingerprint acquisition system*
In this setup, the STM32's MODE1 pin is connected to VCC, and MODE0 is grounded, placing the STM32 in SPI master mode. The FPS200 operates in SPI slave mode. The STM32 connects to the four SPI pins of the FPS200 via PB12–PB15. The STM32 sends commands, addresses, and data to the FPS200 through the SPI interface, and the sensor returns the collected fingerprint data for further processing.
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Yizheng Beide Material Co., Ltd. , https://www.beidevendor.com