A subcutaneous fingerprint recognition system based on Zynq and OCT technology

Bin Yu; Dongsheng He; Zhengjun Tang; Jian Guo; Kaihong Chen; Zhifang Li; Shulian Wu

doi:10.1117/12.3036210

20 November 2024 A subcutaneous fingerprint recognition system based on Zynq and OCT technology

Bin Yu, Dongsheng He, Zhengjun Tang, Jian Guo, Kaihong Chen, Zhifang Li, Shulian Wu

Proceedings Volume 13242, Optics in Health Care and Biomedical Optics XIV; 1324207 (2024) https://doi.org/10.1117/12.3036210
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Abstract

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.

Citation Download Citation

Bin Yu, Dongsheng He, Zhengjun Tang, Jian Guo, Kaihong Chen, Zhifang Li, and Shulian Wu "A subcutaneous fingerprint recognition system based on Zynq and OCT technology", Proc. SPIE 13242, Optics in Health Care and Biomedical Optics XIV, 1324207 (20 November 2024); https://doi.org/10.1117/12.3036210

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