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DSP Implementation Scheme of Fingerprint Identification

Time:2022-09-28 Views:1754
1 Introduction
    Fingerprint identification technology can reliably identify individuals by analyzing local features of fingerprints and extracting detailed feature points from them. Fingerprint identification not only has many unique information security advantages, but also has high practicability and feasibility.
    At present, most fingerprint recognition systems collect fingerprint images into computers and use computers for recognition. Independent fingerprint identification systems produced by some foreign companies are expensive. These all limit the popularization of fingerprint identification technology. Therefore, the research and development of fast, high recognition rate and cheap independent fingerprint identification system has great market prospects and important scientific research value.
    In this paper, a new fingerprint identification system based on DSP is proposed. In hardware, the high-speed processing capability of DSP is used to build a high-speed data processing platform. In software, referring to the processing characteristics of DSP and hardware logic, the traditional fingerprint algorithm is improved to meet the real-time and reliability requirements.
2 Hardware System Structure
    The principle block diagram of the system is shown in Figure (1):
Figure (1) System Structure Block Diagram
The whole system can be divided into three parts: image acquisition module, image processing and recognition module and output module.
2.1 Image acquisition module
    In the image acquisition module, because the fingerprint identification system does not need to observe the image in real time, the requirements for the sensor are not very high, and the general black and white digital CMOS sensors can meet the requirements. In this system, a 3 million pixel high-definition black and white sensor is used as an image acquisition device, which is very suitable for use as a fingerprint image sensor. The advantages of CMOS devices such as low cost, high resolution and good reliability are mainly considered. The disadvantage is that the imaging quality may deteriorate when the finger is sweaty or cracked. In the process of image recognition, the enhancement algorithm based on GABOR is adopted, which can basically overcome the impact caused by this.
2.2 Image processing and recognition module
    The structure of the image processing and recognition module is related to the overall performance of the system. The FPGA+DSP architecture is conducive to building an efficient data processing process and facilitating the allocation of processing tasks, and improving the parallelism and resource utilization of the system. SRAM, SDRAM and FLASH in the system are directly connected to DSP for use: FLASH is used to store programs and some fixed table data; SDRAM, as the system memory of DSP, is used to run system programs; SRAM is a high-speed data storage area used to store temporary variables generated by program operation. DDR SDRAM is specially used to store some high-capacity data blocks, such as fingerprint data collected and pixel gradient data calculated during preprocessing. It is directly connected to FPGA and is the fastest memory area in the system. In addition to serving as the expansion bus interface of DSP processor, FPGA also shares some data processing tasks, because only one DSP is not competent for all computing and control tasks. When processing fingerprint data, it often encounters some tedious addition and subtraction operations and logic operations. Usually, this part is processed by FPGA. Considering the particularity of fingerprint processing algorithm, it is also necessary to realize DDR control function.
    Due to the large amount of mathematical operations in the process of fingerprint identification, the program design inevitably requires a large storage space. In order to improve the overall performance, the heavy computing tasks need to be handed over to the DSP for processing, while the image acquisition part needs to occupy as little DSP time as possible. In addition, using the interval of image acquisition or at the same time of image acquisition, hardware can complete some simple and tedious operations to share the processing tasks of DSP, improve the parallelism of processing, and meet the requirements for real-time. The system uses TMS320VC5402, which has a fast computing speed and high cost performance. The 8bits gray scale fingerprint image collected in the system occupies one byte per pixel, and the image size is 512 × 512 pixels in size, 256k bytes of storage space is required to store a frame image. DSP unit is the core of the whole fingerprint processing system, which is responsible for real-time fingerprint processing.
2.3 Output module
    As an independent fingerprint identification system, the data identified by the system can be directly displayed on the LCD. In the design of the system, the system can also be used as a terminal, that is, the Ethernet interface is extended through FPGA as a large fingerprint identification system terminal that needs to transmit fingerprint database data through the network.
3 Fingerprint identification algorithm
    Fingerprint identification algorithm is the core of fingerprint identification. The flow of fingerprint identification algorithm used in this system is shown in Figure (2).
Figure (2) Fingerprint identification algorithm flow
    Image enhancement is the core problem to be solved in fingerprint image preprocessing. The main purpose of fingerprint image enhancement is to eliminate noise, improve image quality and facilitate feature extraction. Because the fingerprint is composed of ridges and valleys. These textures contain a lot of information, such as texture direction, texture density and so on. Such information is different in different areas of the fingerprint image. Fingerprint image enhancement algorithm is realized by using the regional difference of image information. Traditional fingerprint image enhancement uses the texture direction information of the image to construct a directional filter template to achieve filtering. The conflict between the simplicity of filter construction and the complexity of fingerprint image limits its effectiveness. This system refers to the texture frequency information of fingerprint image, and uses GABOR transform, the optimal filter that can simultaneously analyze the direction of image local structure and spatial frequency, as the template of the filter, thus greatly improving the effect of the enhancement algorithm.
3.1 Ridge line direction
    Except for the singular area, the texture of the fingerprint image is approximately parallel to each other in a small enough area, which is the directional feature of the fingerprint image. Directional feature is one of the most obvious features in fingerprint images. It directly reflects the basic morphological features of fingerprint images in a simplified form. Therefore, it is widely used in fingerprint image classification, enhancement, feature extraction and other aspects.
The method for extracting ridge direction is:
    ⑴ The fingerprint image is divided into sub blocks that are small enough to meet the condition that the textures in the blocks are approximately parallel.





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