Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption
In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated b...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2128 |
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| author | Wangbiao Li Bo Zhang Xiaoman Zhang Bin Liu Hui Li Shulian Wu Zhifang Li |
| author_facet | Wangbiao Li Bo Zhang Xiaoman Zhang Bin Liu Hui Li Shulian Wu Zhifang Li |
| author_sort | Wangbiao Li |
| collection | DOAJ |
| description | In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis–dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption. |
| first_indexed | 2024-04-24T10:34:47Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:34:47Z |
| publishDate | 2024-03-01 |
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| spelling | doaj.art-9f95f4fe67284e4ead7447465308d23a2024-04-12T13:26:17ZengMDPI AGSensors1424-82202024-03-01247212810.3390/s24072128Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image EncryptionWangbiao Li0Bo Zhang1Xiaoman Zhang2Bin Liu3Hui Li4Shulian Wu5Zhifang Li6The Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, ChinaIn this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis–dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption.https://www.mdpi.com/1424-8220/24/7/2128epidermal thicknesscross-sectional OCT imageconvolutional neural networksmaximum intensity projection |
| spellingShingle | Wangbiao Li Bo Zhang Xiaoman Zhang Bin Liu Hui Li Shulian Wu Zhifang Li Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption Sensors epidermal thickness cross-sectional OCT image convolutional neural networks maximum intensity projection |
| title | Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption |
| title_full | Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption |
| title_fullStr | Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption |
| title_full_unstemmed | Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption |
| title_short | Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption |
| title_sort | pressure insensitive epidermal thickness of fingertip skin for optical image encryption |
| topic | epidermal thickness cross-sectional OCT image convolutional neural networks maximum intensity projection |
| url | https://www.mdpi.com/1424-8220/24/7/2128 |
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