SIFT-based algorithm for fingerprint authentication on smartphone

Yamazaki, Masao; Li, Dongju; Isshiki, Takaaki; Kunieda, Hiroaki

doi:10.1109/ictemsys.2015.7110836

Cited by 17 publications

(9 citation statements)

References 5 publications

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“…Yamazaki et al proposed a method that uses the scale-invariant feature transform (SIFT) to extract features from partial fingerprints captured by sensors to register complete fingerprints on a smartphone. The same features in each image are matched and combined to create a single whole fingerprint [4]. Nogueira et al proposed a method that uses a CNN to improve the performance of features extraction for fingerprint liveness detection [5].…”

Section: Related Workmentioning

confidence: 99%

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

et al. 2020

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Existing methods for iris, fingerprint, and 3D face recognition in mobile devices have constraints in terms of price and size owing to their use of additional cameras, lighting, and sensors. Additionally, visible light, camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print recognition are difficult to be used in mobile devices due to limitations in recognition performance and user inconvenience. In response to these problems, studies have been conducted on finger wrinkle recognition in mobile devices; however, image quality is often reduced by motion blurring caused by the movement of the camera or the user's finger, thereby reducing recognition performance. This study proposes a method for restoring and recognizing motion-blurred finger wrinkle images based on a generative adversarial network and deep convolutional neural network. Experiments were performed using two types of finger wrinkle databases, which were custom-made from images of 33 people captured by smart phone cameras (Dongguk mobile finger wrinkle database versions 1 and 2, denoted as DMFW-DB1 and DMFW-DB2, respectively). The results demonstrated high restoration and recognition performance in comparison with the state-of-the-art methods. INDEX TERMS Biometrics, finger wrinkle recognition, generative adversarial network (GAN), restoration of motion blurred image.

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Section: Related Workmentioning

confidence: 99%

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

et al. 2020

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“…The key feature of the proposed approach is that it was robust to noise. Masao Yamazaki et al [26] presented a scale invariant feature transform (SIFT) approach for fingerprint authentication for smart phones. In proposed approach two different images for the same finger were merged to replace the conventional minutiae method.…”

Section: Short Review On Fingerprint Recognition Techniquesmentioning

confidence: 99%

Wavelet Based Fingerprint Authentication System: A Review

Verma¹

2016

ELELIJ

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Biometric recognition refers to the use of biological characteristics for identification and verification of individuals. Use of biometric is increasing nowadays because biometric characteristics are difficult to replicate and lifelong. Applications which require only the authorized persons to access the resources are information systems, National id systems, voter and driver registrations, documentations, military area and ATMs. Among all biometrics, fingerprints is the most widely used and accepted by the user as the acquisition of fingerprint image is minimally invasive and require little hardware. Traditional fingerprint identification algorithm based on Minutiae Extraction, Discrete cosine transform(DCT) and fast Fourier transform require so much computation. Wavelet based algorithm do not require any preprocessing and post processing steps and hence is the key for low cost fingerprint identification system. This paper presents the overview of different fingerprint recognition system. In comparison to older fingerprint recognition system that is based on minutiae and FFT, the wavelet based system has high recognition rates.

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“…To update any of the listed factors the user has to start device by passing fingerprint biometric authentication [11] and select finger print manager in settings. User select any one of choice like to update fingerprint, update back-up code, update alternate email-id, update security question.…”

Section: Figure 1: Registration Of Factorsmentioning

confidence: 99%

Security for Android Mobile Phones using Biometric Authentication against Factory Reset

2015

IJSR

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Android Mobile devices are secured using various mechanisms like pattern, pin, and password. Biometric Authentication is an upcoming technology accepted by various android mobile manufactures for better security and with change in technology, time this mechanisms are vulnerable to various attacks like factory reset. Factory Reset will erase all the settings in the device and makes device vulnerable for hackers to misuse it. This paper proposes a mechanism to overcome against hard reset as well as factory reset through settings by allowing person to perform factory reset operation only after passing biometric authentication and using correct back-up code. Mobile device can be more secured by using biometric authentication with other factors like security question, alternate email-id, and authentication code generation algorithm.

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SIFT-based algorithm for fingerprint authentication on smartphone

Cited by 17 publications

References 5 publications

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

Wavelet Based Fingerprint Authentication System: A Review

Security for Android Mobile Phones using Biometric Authentication against Factory Reset

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