With the advancement of technology and increasing security demands, the exploration and extraction of new internal fingertip features have become a significant trend. In traditional fingerprint recognition systems, enhancing anti-spoofing capabilities is crucial. Conventional fingerprints are typically obtained through surface imaging, making their texture features easily susceptible to theft. Optical coherence tomography (OCT) technology offers non-invasive, high-resolution, and live tissue detection advantages, providing micron-level resolution images of biological tissues within a millimeter depth range. This enables the capture of more secure and stable internal biometric features such as internal fingerprints, sweat pores, and sweat glands. Subcutaneous fingerprints are stable, difficult to alter, and possess strong anti-spoofing characteristics. Consequently, subcutaneous fingerprint recognition promises higher security and reliability, addressing the shortcomings of currently prevalent fingerprint recognition systems. This paper presents a subcutaneous fingerprint recognition scheme based on an embedded system. The scheme utilizes Xilinx's Zynq, an all-programmable System on Chip (SoC), and employs OCT technology for fingerprint capture to meet the reliability demands of fingerprint recognition. It addresses issues in traditional OCT capture systems, such as large size, high power consumption, and poor scalability. By using image processing algorithms such as Gray level Co-occurrence Matrix(GLCM), the system extracts features from subcutaneous fingerprint images, achieving low-cost, real-time subcutaneous fingerprint image capture and recognition.
KEYWORDS: Optical coherence tomography, Image processing, Data processing, Data acquisition, Data conversion, Spectral data processing, Computing systems, Signal processing, Image restoration
Optical Coherence Tomography(OCT) system is a non-contact imaging modality based on low-coherence optical interferometry, used for imaging turbid scattering media. They excel in rendering depth-resolved images of internal structures with micrometer-scale resolution. Previous OCT systems have some defects in image reconstruction, which are limited by complex signal processing and mathematical computation, slow image processing speed, difficult to realize real-time imaging, and high equipment cost. This paper proposes an OCT image reconstruction algorithm acceleration scheme based on the combination of FPGA (Field-Programmable Gate Array) and Python, aiming at accelerating and simplifying the image acquisition and processing of the OCT system through Python, so as to enhance the efficiency of medical diagnosis and biological research. Using Python as the upper computer control software, provide user-friendly graphical interface, output spectral waveform and then realize the Fourier transform, de-direct current and autocorrelation terms and other algorithmic steps to generate OCT images, to realize the real-time data transmission and processing. Python not only has a powerful data visualization ability, but also has the advantages of simple operation, easy to develop the program to ensure that the system operates efficiently.
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