Analysis of 3D signatures recorded using leap motion sensor

Behera, Santosh Kumar; Dogra, Debi Prosad; Roy, Partha Pratim

doi:10.1007/s11042-017-5011-4

Cited by 25 publications

(18 citation statements)

References 35 publications

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“…Recently, other works have been developed related to the in-air signature, as (Behera et al, 2017), which also proposes the use of the handwritten signature, with the difference of the sensor used to capture the data. Another example is shown in Behera, Dash et al (2018), , where a system based on 3D in-air signature is presented as solution for verification and recognition.…”

Section: In-air Information Applied To Signature Verificationmentioning

confidence: 99%

“…User authentication based on signatures is a widely-studied field in biometrics since it is one of the most accepted verification techniques (Behera et al, 2017;Impedovo & Pirlo, 2008), mainly because of its usability. Signatures verification techniques are usually divided into online (Plamondon & Srihari, 2000) and offline (Vargas et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…As shown in Behera et al (2017), Impedovo and Pirlo (2008), Lee et al (1996), some signature authentication systems are not resistant to imposter attacks because they do not contemplate these kind of errors. For these reasons, authentication systems should also verify if a signature is genuine or not, to ensure their robustness and efficiency against any kind of attack.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, 3D representations of the signature have already come to reality, performing better results. The 3D representations can be developed by using different sensors, as accelerometers (Bailador et al, 2011), Microsoft Kinect (Qu, 2015), Leap Motion Sensor (Behera et al, 2017), etc.…”

Section: Introductionmentioning

confidence: 99%

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In-air signature verification system using Leap Motion

Guerra-Segura

Ortega-Pérez

Travieso

2021

Expert Systems with Applications

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Section: In-air Information Applied To Signature Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In-air signature verification system using Leap Motion

Guerra-Segura

Ortega-Pérez

Travieso

2021

Expert Systems with Applications

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“…Kane and Khanna (Kane and Khanna, 2017) have proposed an equipolar signature (EPS) technique for a vision-based mid-air unistroke character input framework that is resistant to variations in scale, translation and rotation. Some recent works have also addressed the problem of 3D air signature recognition and verification (Behera et al, 2017(Behera et al, , 2018. (Behera et al, 2018) presents a method for analyzing 3D air signatures captured using the Leap Motion Controller, with the help of a new geometrical feature extracted from the convex hull enclosing a signature.…”

Section: Air-writing Recognitionmentioning

confidence: 99%

Fingertip detection and tracking for recognition of air-writing in videos

Mukherjee

Sekh

Dogra

et al. 2019

Expert Systems with Applications

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Air-writing is the process of writing characters or words in free space using finger or hand movements without the aid of any hand-held device. In this work, we address the problem of mid-air finger writing using web-cam video as input. In spite of recent advances in object detection and tracking, accurate and robust detection and tracking of the fingertip remains a challenging task, primarily due to small dimension of the fingertip. Moreover, the initialization and termination of mid-air finger writing is also challenging due to the absence of any standard delimiting criterion. To solve these problems, we propose a new writing hand pose detection algorithm for initialization of air-writing using the Faster R-CNN framework for accurate hand detection followed by hand segmentation and finally counting the number of raised fingers based on geometrical properties of the hand. Further, we propose a robust fingertip detection and tracking approach using a new signature function called distance-weighted curvature entropy. Finally, a fingertip velocity-based termination criterion is used as a delimiter to mark the completion of the air-writing gesture. Experiments show the superiority of the proposed fingertip detection and tracking algorithm over state-of-the-art approaches giving a mean precision of 73.1 % while achieving real-time performance at 18.5 fps, a condition which is of vital importance to air-writing. Character recognition experiments give a mean accuracy of 96.11 % using the proposed air-writing system, a result which is comparable to that of existing handwritten character recognition systems. IntroductionWith the emergence of virtual and augmented reality, the need for the development of natural human-computer interaction (HCI) systems to replace the traditional HCI approaches is increasing rapidly. In particular, interfaces incorporating hand gesturebased interaction have gained popularity in many fields of application viz. automotive interfaces (Ohn-Bar and Trivedi, 2014), human activity recognition (Rohrbach et al., 2016) and several state-of-the-art hand gesture recognition approaches have been developed (Molchanov et al., 2015;Rautaray and Agrawal, 2015). However, hand motion gestures as such are not sufficient to input text. This necessitates the need for the development of touch-less air-writing systems which may replace touch and electromechanical input panels leading to a more natural human-computer interaction (HCI) approach.A vision-based system for the recognition of mid-air finger-writing trajectories is not a new problem and substantial work has been done in the past two decades. One of the early works by Oka et al. (Oka et al., 2002) used a sophisticated device with an infrared and color sensor for fingertip tracking and recognition of simple geometric shapes trajectories. In (Amma et al., 2012), inertial sensors attached to a glove were used for continuous spotting and recognition of air-writing. Recently, Misra et al. (Misra et al., 2017) have developed a hand gesture recognition framewor...

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Scale‐free topology security mechanism of wireless sensor network against cascade failure

Gao

Liu

et al. 2021

Int J Communication

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As the basic architecture of the Internet of things, wireless sensor networks are the most important technical means to detect data on the Internet of things, and their security is essential to the development and implementation of the Internet of things. The wireless sensor network based on the scale-free theory has strong fault tolerance when random nodes fail and can better adapt to the unsupervised and complex environment of wireless sensor networks.However, in a wireless sensor network with scale-free function, the unevenness of its degree distribution causes some nodes to be very critical in the network. Once attacked, it will cause major damage to the network. Selective promotion attacks are relatively common. At the network level, the attacker destroys the normal collection of network data by selectively losing information or not forwarding sensitive information, which is extremely harmful to the network using this function. In order to overcome the influence of excessive dependence on a single path on the balance performance of the network, starting from the entire network, a secure multi-path routing algorithm for wireless sensor networks based on scale-free topology is proposed. Through the analysis of the network topology, the highest degree network node can be found. Optimize the load factor, use digital watermarking technology to locate the network path packet loss rate, and perform Gaussian modeling on the packet loss rate. Then the routing loading function and the routing packet loss rate are used as the confidence model factors to create the routing confidence model. We must first establish a model, select the transmission path according to the received function, and then obtain the global optimal security sensor routing algorithm. Through the algorithm in this thesis, the security of the wireless sensor network is improved by 17%.

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Analysis of 3D signatures recorded using leap motion sensor

Cited by 25 publications

References 35 publications

In-air signature verification system using Leap Motion

In-air signature verification system using Leap Motion

Fingertip detection and tracking for recognition of air-writing in videos

Scale‐free topology security mechanism of wireless sensor network against cascade failure

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