Design and Implementation of Fingerprint Identification System Based on ARM9
Biometric technology uses the inherent physiological characteristics of the human body (such as fingerprints, faces, red films, etc.) and behavioral characteristics (such as handwriting, sound, gait, etc.) to identify individual identities.
Biometrics are more secure, confidential, and convenient than traditional methods of identification. Biometrics technology has the advantages of being easy to forget, anti-counterfeiting performance, not easy to forge or stolen, "carrying" with you and being available anytime, anywhere.
The principle of biometric identification is to use biometric devices to sample biometric features, extract their unique features and convert them into digital codes, and further form these codes into feature templates. When people interact with the identification device for identity authentication, the identification device Get the characteristics and compare them with the feature templates in the database to determine if they match, and then decide to accept or reject the person. Among the many biometric technologies used for authentication, fingerprint recognition technology is currently the most convenient, reliable, non-invasive and inexpensive solution.
As the most obvious appearance feature in the human body, fingerprint has the advantages of uniqueness, universality, uniqueness and easy collection. Fingerprint identification technology utilizes the physiological characteristics of human fingerprint stability and uniqueness as a kind of “living ID card†of people, and the fingerprint has irreplaceability, which greatly improves the security of identification by fingerprint. With the development of image processing pattern recognition methods and the maturity of fingerprint sensor technology, fingerprint identification methods have good application prospects in the fields of finance, public security, access control, and household registration management. The collection of fingerprints is relatively easy; the fingerprint recognition algorithm is relatively mature. Because fingerprint recognition has the advantages of fast, convenient and miniaturized scanning fingerprints, fingerprint recognition technology has gradually entered the civilian market and applied to many embedded devices, but how to improve the recognition rate and stability of fingerprint recognition system and reduce costs As well as extended stability and node distribution, there are a series of technical challenges.
Therefore, this paper studies the microprocessor AT91SAM7X256 with the arm core as the core, and the external extended fingerprint sensor MBF200 constitutes the fingerprint identification server hardware; the system software transplants the real-time multitasking operating system μC/OS-II, file system, LwIP, application software Fingerprint recognition. The method has the characteristics of low cost, low resource consumption and strong scalability.
1 Distributed fingerprint identification system principle and hardware design
Fingerprint recognition technology mainly involves four functional modules: reading fingerprint images, extracting features, saving data and comparing. The image of the human body fingerprint is read by the fingerprint reading device, and then the original image is initially processed to make it clearer, and the fingerprint feature data is established by the fingerprint identification software. The software finds the data points called "nodes" (minuTIae) from the fingerprint, that is, the coordinate positions of the bifurcation, termination or looping of the fingerprint lines, which have more than 7 unique features at the same time. Usually there are 70 nodes on the average finger, so this method will generate about 500 data. These data are often referred to as templates. A method of fuzzy comparison by computer. Compare the templates of the two fingerprints, calculate their similarity, and finally get the matching results of the two fingerprints.
The implementation of the hardware circuit is based on the microprocessor AT91SAM7X256. The peripheral circuits mainly include the fingerprint identification module MBF200, the Ethernet physical layer (PHY) transceiver RTL8201BL, the large-capacity data FlashAT45DBl61D, the hardware calendar clock device DSl302, the power supply circuit, the reset and the clock. The circuit is shown in Figure 1.
1.1 AT91SAM7X256 device and MBF200 module application
The AT91SAM7X256 is a 32-bit arm7TDMI-based microprocessor from ATMEL. It also integrates 256 kh of on-chip Flash and 64 kb of SRAM on a single chip, eliminating the need for external expansion memory. It also integrates USB2.0 device ports and rich on-chip peripheral resources. The reset controller of the AT9lSAM7X256 can manage the power-on sequence of the chip and the entire system. The microcontroller features embedded 10/100 Mb/s Ethernet (MAC) MAC, CAN, full-speed (12 Mb/s) USB 2.0, designed for a wide range of networked real-time embedded systems with stable performance and features. Powerful, can be widely used in the field of protocol conversion, communication, and industrial control. The application of AT91SAM7X256 to develop a fingerprint identification system can effectively control costs. Industrial networks require extreme stability, but experiments have shown that more than 60% of bus bandwidth usage creates conflicts.
MBF200 is an advanced solid-state fingerprint sensor introduced by Fujitsu. In addition to automatic fingerprint detection, it also has a variety of interface modes. It is a capacitive sensor. Its capacitive sensor array consists of two-dimensional metal electrodes, all metal electrodes. Acting as a capacitive plate, the contact fingers act as the second capacitive plate, and the passivation layer on the surface of the device acts as an insulating layer for the two plates. When a finger touches the surface of the sensor, the unevenness of the fingerprint creates a varying capacitance on the sensor array, causing a change in voltage across the two-dimensional array and forming a fingerprint sensing image. A capacitive solid-state device using standard C13MS technology with a resolution of 500 dpi and a sensor area of ​​1.28 cmxl. 50 cm. It has automatic fingerprint detection capability and includes 8-bit analog-to-digital converter, which can provide three kinds of bus interface forms. The power consumption at 5 V operating voltage is less than 70 mW.
1.2 Ethernet interface circuit design
The AT91SAM7X256 integrates a MAC controller internally to support the MII interface and the RMII interface. RTL820lBL is an industrial grade 10/100 Mb/s low-power Ethernet transceiver with MII interface, 25 MHz clock output, intelligent power-down mode, providing a stable and reliable high-quality network solution for the factory. And other harsh operating environments to set up Ethernet that supports real-time transmission, in accordance with IEEE
The technical standard of 802.3u. The schematic diagram of the Ethernet interface circuit is shown in Figure 2.
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