**Introduction**
Fingerprint recognition is a biometric technology that identifies individuals based on the uniqueness and stability of their fingerprint patterns. As society continues to evolve, embedded fingerprint recognition systems have gained increasing popularity in the market, becoming a key area of research and development in recent years. However, many existing embedded fingerprint algorithms still face challenges in terms of real-time performance and accuracy, which necessitates further optimization for more reliable and efficient identification. This study aims to design and implement a fingerprint recognition system using an STM32 microcontroller. The system collects fingerprint data via a sensor, processes it using a fingerprint algorithm, and integrates a human-computer interaction interface built on the VC++ platform to display the captured fingerprint images.
**1. System Hardware Design**
**1.1 Structure Composition and Features**
This system employs the STM32F-103ZET6 microcontroller, which is based on the ARM Cortex-M3 core. The chip uses 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 system's hardware structure and functional block diagram are illustrated 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 overall workflow begins with the system receiving a 5V regulated power supply through USB, which is then converted to 3.3V internally. Once powered on, the STM32 initializes the sensor registers, and the FPS200 fingerprint sensor collects the image data via the SPI interface. The STM32 communicates with the sensor, stores the fingerprint image in SRAM, performs preprocessing, and extracts feature points for matching. Finally, the system outputs the recognition result. Additionally, the STM32 sends the fingerprint data to the PC via UART for display. Since one image requires 76.8 KB of memory, the internal RAM is insufficient, so external SRAM is used for storage. The system program and fingerprint templates are stored in FLASH, allowing for easy updates during use. The main controller uses a JTAG interface for simulation debugging with IAR for ARM. **1.2 Fingerprint Acquisition Circuit Design** The quality of fingerprint image acquisition plays a critical role in the overall performance of the system. High-quality fingerprint images improve the efficiency of subsequent image processing algorithms, reduce complexity, and enhance the accuracy of the recognition system. The FPS200 fingerprint sensor, developed by Veridicon, offers a resolution of 500 dpi, with a 300×256 sensor array, producing 256-level grayscale images with 8-bit pixel data. It supports multiple interface modes, including MCU, SPI, and USB. In this design, the SPI interface is used due to its simplicity. The sensor operates at 3.3V and is connected to the STM32 via the PB12 to PB15 pins. The STM32 is set to SPI master mode (MODE1 connected to VCC, MODE0 grounded), while the FPS200 works in SPI slave mode. Through the SPI interface, the STM32 sends commands, addresses, and data to the FPS200, which then returns the collected fingerprint data for further processing.
Figure 2: Schematic of the Fingerprint Acquisition System
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