The Effectiveness of Dynamic Features of Finger Based Gestures on Smartphones’ Touchscreens for User Identification

Al-Showarah, Suleyman

doi:10.3991/ijim.v11i1.6368

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…However, using the dedicated pen when signing is inconvenient for users. In recent years, it becomes general to write (touch) directly by a finger on a touchscreen instead of using the stylus pen [12][13][14][15][16][17][18][19][20][21][22][23]. However, to write a signature with a finger on a small touchscreen of a smartphone is very inconvenient for users.…”

Section: Introductionmentioning

confidence: 99%

A Smartphone User Verification Method Based on Finger-Writing of a Simple Symbol

Takahashi

Masegi

Nakanishi

2021

HCI International 2021 - Posters

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Writer verification based on finger-writing of a simple symbol on a touch screen is proposed herein. The users write a simple and well-known symbol, for example, a circle, triangle, or square. In addition, the users write the symbol using their finger instead of a pen on a tablet. This allows more convenience with the use of the proposed method. However, it was observed that the original approach and obtained verification performance were not reliable. In this work, we create a new database using thirty participants. By examining individual features extracted from the database, the risk of misjudgment is found. In order to solve this problem, a coordinate transformation method is introduced. Moreover, normalization is examined for fusing individual features by comparing three normalization methods. The proposed method with appropriate coordinate transformation and normalization achieves an equal error rate of 10.6% even when all participants write only a simple circle.

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Section: Introductionmentioning

confidence: 99%

A Smartphone User Verification Method Based on Finger-Writing of a Simple Symbol

Takahashi

Masegi

Nakanishi

2021

HCI International 2021 - Posters

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“…Many classifiers can be applied to recognize the handwritten Arabic text. For example, the Artificial Neural Network (ANN), the Support Vector Machine (SVM), the k-nearest neighbors (kNN) and the Hidden Markov Model (HMM) classifiers [3,[30][31][32].…”

Section: Introductionmentioning

confidence: 99%

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

AL-Shatnawi

Al-Saqqar

Alhusban

2020

Int. J. Interact. Mob. Technol.

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In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively.

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“…As both the penetration of smart devices and the use of large-screen smart devices have grown, the importance of the touch interface, the most typical input method for smart devices, has also increased. To support the statement, most of the children who had participated in the survey had positive opinions for using their fingers to touch the screen [2] andthere is a research trying to identify what the gestures are given users' inputs [3].A typical experiment that evaluates usability between pinch and force touchhad conducted with elderly people and the result showed they spent less time and had no difficultyin completing a task with force touch [4]. Althoughmost of them was not familiar with new touch technology and perhaps they are not willing to accept the new technology, youngergenerationtends to havea positive attitude on it when it gives them convenience.…”

Section: Introductionmentioning

confidence: 99%

Defining Stable Touch Area Based on a Large-Screen Smart Device in 3D-Touch Interface

Seo¹,

Shin²,

Nam³

2019

Int. J. Interact. Mob. Technol.

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Touch interface technologies for mobile devices are essentially in use. The purpose of such touch interfaces is to run an application by touching a screen with a user’s finger or to implement various functions on the device. When the user has an attempt to use the touch interface, users tend to grab the mobile device with one hand. Because of the existence of untouchable areas to which the user cannot reach with the user’s fingers, it is possible to occur for a case where the user is not able to touch a specific area on the screen accurately. This results in some issues that the mobile device does not carry out the user’s desired function and the execution time is delayed due to the wrong implementation. Therefore, there is a need to distinguish the area where the user can stably input the touch interface from the area where the users cannot and to overcome the problems of the unstable touch area. Furthermore, when the size of the screen increases, these issues will become more serious because of an increase in the unstable touch areas. Especially, an interface that receives position and force data like 3D-touch requires the stable area setting different from the conventional 2D-touch. In this paper, we search and analyze the stable touch areas on the large screen where the user can do 3D-touch inputs.

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The Effectiveness of Dynamic Features of Finger Based Gestures on Smartphones’ Touchscreens for User Identification

Cited by 4 publications

References 10 publications

A Smartphone User Verification Method Based on Finger-Writing of a Simple Symbol

A Smartphone User Verification Method Based on Finger-Writing of a Simple Symbol

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

Defining Stable Touch Area Based on a Large-Screen Smart Device in 3D-Touch Interface

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