A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging

Tang, Kai; Liu, Jessica Aijia; Wang, Wei; Li, Pengfei; Chen, Xi

doi:10.3390/s18093050

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…This indoor positioning system based on the iBeacon has an iPhone operating system based device attached that receives the radio wave from the iBeacon, which is transmitted through a Bluetooth device installed in the environment to the positioning object, which acts as a receiver [ 67 , 68 ]. This indoor positioning system uses the fingerprinting method, which estimates the position coordinates of the positioning object by comparing the value of the reception strength (received signal strength indicator (RSSI)) as in the case of the conventional system using BLE [ 69 , 70 , 71 ]. When the receiver receives the surrounding Bluetooth radio waves, it will self-localize with the data sets of the received strength associated with the position coordinates in the environment in advance.…”

Section: Indoor Positioning Experiments In An Environment Surroundementioning

confidence: 99%

Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Zempo

Arai

Aoki

et al. 2020

Sensors

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To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In this paper, a sensing framework for a VCC process in retail stores is proposed by improving an environment recognition based indoor positioning system with high positioning performance in a metal shelf environment. The conventional indoor positioning systems use radio waves; therefore, errors are caused by reflection, absorption, and interference from metal shelves. An improvement in positioning performance was achieved in the proposed method by using an IR (infrared) slit and IR light, which avoids such errors. The system was designed to recognize many and unspecified people based on the environment recognition method that the receivers had installed, in the service environment. In addition, sensor networking was also conducted by adding a function to transmit payload and identification simultaneously to the beacons that were attached to positioning objects. The effectiveness of the proposed method was verified by installing it not only in an experimental environment with ideal conditions, but posteriorly, the system was tested in real conditions, in a retail store. In our experimental setup, in a comparison with equal element numbers, positioning identification was possible within an error of 96.2 mm in a static environment in contrast to the radio wave based method where an average positioning error of approximately 648 mm was measured using the radio wave based method (Bluetooth low-energy fingerprinting technique). Moreover, when multiple beacons were used simultaneously in our system within the measurement range of one receiver, the appropriate setting of the pulse interval and jitter rate was implemented by simulation. Additionally, it was confirmed that, in a real scenario, it is possible to measure the changes in movement and positional relationships between people. This result shows the feasibility of measuring and evaluating the VCC process in retail stores, although it was difficult to measure the interaction between actors.

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Section: Indoor Positioning Experiments In An Environment Surroundementioning

confidence: 99%

Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Zempo

Arai

Aoki

et al. 2020

Sensors

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“…Conventional hand texture-based biometrics have been used for fingerprint recognition, finger-knuckle-print recognition using the inherent skin patterns of the outer surface around the phalangeal joints, palmprint recognition using the details of raised areas (ridges) and branches (bifurcations) on a human palm, and finger-wrinkle recognition. The most studied and commercially used method is fingerprint recognition [3], [4]. AlShehri et al analyzed the characteristics of fingerprints acquired by different sensors, where cross-sensor matching is a challenging problem [5].…”

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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“…The main tasks of CV are acquiring, processing, analyzing, and understanding the environment through digital images [ 1 ]. Some high-level problems which are successfully tackled by CV are optical character recognition (OCR) [ 2 , 3 , 4 ], machine vision inspection [ 5 , 6 ], 3D model building (photogrammetry) [ 7 , 8 ], medical imaging [ 9 , 10 ], automotive safety [ 11 ], motion capture, surveillance [ 12 ], fingerprint recognition [ 13 , 14 ], face recognition, and gesture recognition [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Orhei

Vert

Mocofan

et al. 2021

Sensors

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Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

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A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging

Cited by 14 publications

References 19 publications

Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

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

End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

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