Advances in Fingerprint Sensors Using RF Imaging Techniques

Setlak, Dale

doi:10.1007/0-387-21685-5_2

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…Identity protection is of ever-higher importance in today's world, increasing the use of fingerprint sensors, both in the context of governmental and personal use. Many different sensing methods can be used for fingerprint detection: capacitive, ultrasound, RF, optical, thermal, .... [1][2][3]. However, most realizations are fabricated on a Si-CMOS technology platform, making them expensive, rigid and only available in small sizes.…”

Section: Introductionmentioning

confidence: 99%

14‐1: Flexible Large‐Area Multi‐Fingerprint Sensors based on Thermal Mass Detection

Roose

Mainguet

Gallaire

et al. 2020

Symp Digest of Tech Papers

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Section: Introductionmentioning

confidence: 99%

14‐1: Flexible Large‐Area Multi‐Fingerprint Sensors based on Thermal Mass Detection

Roose

Mainguet

Gallaire

et al. 2020

Symp Digest of Tech Papers

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“…Among a variety of biometric techniques, fingerprint recognition is one of the most popular biometric techniques in the current market. For example, recent smartphones commonly adopt fingerprint sensors for a biometric identification method [ 4 , 5 , 6 ]. The key property of fingerprints is that no two individuals have the same fingerprints and aging does not affect the fingerprints [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…There are many fingerprint sensing methods such as optical methods [ 7 , 8 ], thermal methods [ 9 ], ultrasonic methods [ 10 ], RF imaging methods [ 6 ] and capacitive methods [ 11 , 12 , 13 , 14 ]. The capacitive method is the preferred solution for mobile and IoT applications due to the compact size and low power consumption of capacitive fingerprint sensor chips.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, there are many companies that use and develop the stand-alone capacitive fingerprint sensor for cell phone and non-cell phone devices [ 32 ] and our proposed solution is based on this approach. The stand-alone capacitive fingerprint sensor is the mainstream of the fingerprint recognition solutions, which is still used by Samsung and many other phone producers due to its maturity and high performance with a low cost and low power consumption [ 5 , 6 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

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CMOS Capacitive Fingerprint Sensor Based on Differential Sensing Circuit with Noise Cancellation

Hassan

Kim

2018

Sensors

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In this paper, we introduce a differential sensing technique for CMOS capacitive fingerprint detection. It employs a new capacitive-sensing cell structure with charge sharing detection and readout circuit. The proposed technique also can eliminate the effect of parasitic capacitances by employing parasitic insensitive switched-capacitor structure and so increases the sensitivity even under severe noisy conditions. It can also overcome the performance degradation caused by various conditions of finger surface by using a differential integrator and adjusting its number of integrations. In addition, the proposed architecture allows parallel detection of all sensing channels. It can, therefore, substantially speed up the detection process compared with conventional architectures. We implemented a prototype fingerprint sensor chip with an array of 20 × 16 sensor cells using a 130 nm CMOS process. Simulation experiments demonstrated that the proposed architecture provided an SNR gain of 54 dB, whereas a conventional single line sensing gives an SNR gain of only 13 dB.

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“…These devices are categorized into optical, electrical and ultrasonic sensors. [7][8][9][10][11] Due to the presence of injury, dirt or liquid, image produced by the scanning device often experiences noise or distortion which may ultimately degrade the performance of the system. The solution to this is to provide AFIS with mechanism for image enhancement.…”

Section: Introductionmentioning

confidence: 99%

Effect of parameter values on fingerprint filtering

Charles

Babatunde

Cleopas

2016

AIR

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Fingerprint is presently the most significant biometric for human verification and identification. The reason being its highest degree of uniqueness, availability, durability and consistency when compared with other biometrics such as face, nose, iris, ear, palm print and signature. The use of fingerprint in human identity management spans through stages of enrolment, enhancement, feature extraction and pattern matching. The enhancement stage involves ridge segmentation, normalization, orientation estimation, frequency estimation, filtering, binarization and thinning. Filtering is the stage at which all forms of noise and contaminations introduced into the image during enrolment are removed. The removal of noise and contaminations is necessary for accurate feature extraction and pattern matching. In some of the existing fingerprint image filtering algorithms, accurate and appropriate parameter selections are essential for obtaining optimal and satisfactory results. In this research, the existing Gabor filter was modified and the values of some standard parameters were varied. Experimental study on the adequacy of the modified algorithm and its parameter values on fingerprint filtering were investigated on the standard FVC2002 fingerprint database. Comparative analysis of the obtained results with what were obtained from some existing algorithms shows satisfactory and acceptable performances of the modified algorithm.

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Advances in Fingerprint Sensors Using RF Imaging Techniques

Cited by 9 publications

References 3 publications

14‐1: Flexible Large‐Area Multi‐Fingerprint Sensors based on Thermal Mass Detection

14‐1: Flexible Large‐Area Multi‐Fingerprint Sensors based on Thermal Mass Detection

CMOS Capacitive Fingerprint Sensor Based on Differential Sensing Circuit with Noise Cancellation

Effect of parameter values on fingerprint filtering

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