A Novel Palmprint Authentication System Using Level 3 Pore Feature

Priya, Swarn; Ezhilarasan, M.

doi:10.1109/iccsp.2018.8524278

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Jain et al used a fixed-length minutia descriptor, MinutiaCode, to capture the unique information around each minutia and used an alignment-based minutia-matching algorithm to perform palmprint recognition [20]. Priya et al proposed a high resolution palmprint authenticating system based on level 3 pore feature [21]. A study by [22] proposed the palmprint recognition method based on binary wavelet transform and local binary pattern (LBP).…”

Section: Related Workmentioning

confidence: 99%

Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

2019

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Iris, fingerprint, and three-dimensional face recognition technologies used in mobile devices face obstacles owing to price and size restrictions by additional cameras, lighting, and sensors. As an alternative, two-dimensional face recognition based on the built-in visible-light camera of mobile devices has been widely used. However, face recognition performance is greatly influenced by the factors, such as facial expression, illumination, and pose changes. Considering these limitations, researchers have studied palmprint, touchless fingerprint, and finger-knuckle-print recognition using the built-in visible light camera. However, these techniques reduce user convenience because of the difficulty in positioning a palm or fingers on the camera. To consider these issues, we propose a biometric system based on a finger-wrinkle image acquired by the visible-light camera of a smartphone. A deep residual network is used to address the degradation of recognition performance caused by misalignment and illumination variation occurring during image acquisition. Owing to the unavailability of the finger-wrinkle open database obtained by smartphone camera, we built the Dongguk finger-wrinkle database, including the images from 33 people. The results show that the recognition performance by our method exceeds in those of conventional methods. INDEX TERMS Biometrics, finger-wrinkle recognition, smartphone camera, deep residual network.

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Section: Related Workmentioning

confidence: 99%