A Novel Encryption Method for Dorsal Hand Vein Images on a Microcomputer

Yıldız, Mustafa Zahid; Boyraz, Ömer Faruk; Güleryüz, Emre; Akgül, Akif; Hussain, Iqtadar

doi:10.1109/access.2019.2914721

Cited by 20 publications

(13 citation statements)

References 71 publications

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“…Six distance features are used to describe the topological attributes; the four features d v , C2, δ 2 d , and d e are the same as in Section 2, and two new features are included. The new distance score based on the edges, s e , is defined in (11) and (12).…”

Section: Modified Measurement Of Distance Featuresmentioning

confidence: 99%

“…Vein patterns in the dorsal hand are commonly used for biometric recognition and are detected using infrared light from the live body. Compared with other hand-based biometrics, such as fingerprints and palm print, the primary advantages of dorsal vein pattern identification are that no contact is required during image acquisition [1], and it is difficult to falsify the vein pattern [2]. Dorsal hand vein images are more conducive to collect vein pattern than palm, because the anterior surface of the palm is much thicker than the dorsal surface [3].…”

Section: Introductionmentioning

confidence: 99%

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A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

et al. 2020

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Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 − 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands. INDEX TERMS Dorsal hand vein recognition, biometric graph matching, occlusion, databases.

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Section: Modified Measurement Of Distance Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

et al. 2020

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“…That is, an adversary does not stand a chance to extract information from the encrypted image. Formally, we regularly use NPCR (number of pixels change rate), and UACI (uniform average change intensity) tests to evaluate the proper behaviour of image encryption cyphers regarding differential attacks [42]. For computing these two tests, we encrypt two images that in plain-text only differs by one-pixel.…”

Section: Additional Testsmentioning

confidence: 99%

A True Random Number Generator Based on Gait Data for the Internet of You

et al. 2020

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The Internet of Things (IoT) is more and more a reality, and every day the number of connected objects increases. The growth is practically exponential-there are currently about 8 billion and expected to reach 21 billion in 2025. The applications of these devices are very diverse and range from home automation, through traffic monitoring or pollution, to sensors to monitor our health or improve our performance. While the potential of their applications seems to be unlimited, the cyber-security of these devices and their communications is critical for a flourishing deployment. Random Number Generators (RNGs) are essential to many security tasks such as seeds for key-generation or nonces used in authentication protocols. Till now, True Random Number Generators (TRNGs) are mainly based on physical phenomena, but there is a new trend that uses signals from our body (e.g., electrocardiograms) as an entropy source. Inspired by the last wave, we propose a new TRNG based on gait data (six 3-axis gyroscopes and accelerometers sensors over the subjects). We test both the quality of the entropic source (NIST SP800-90B) and the quality of the random bits generated (ENT, DIEHARDER and NIST 800-22). From this in-depth analysis, we can conclude that: 1) the gait data is a good source of entropy for random bit generation; 2) our proposed TRNG outputs bits that behave like a random variable. All this confirms the feasibility and the excellent properties of the proposed generator. INDEX TERMS True random number generator, Internet of Things, gait data, entropy, randomness.

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“…With the tremendous use of visual data, it has also become an important to maintain confidentiality, integrity and authenticity of the visual data. There are number of techniques present to secure the images and encryption is one of them, which is a process of transforming an image to cryptic image by using a key [5].…”

Section: Introductionmentioning

confidence: 99%