User Authentication Through Pen Tablet Data Using Imputation and Flatten Function

Akash, Md. Azim Hossain; Begum, Nasima; Rahman, Sayma; Shin, Jungpil; Amiruzzaman, Md; Islam, Rashedul

doi:10.1109/ickii50300.2020.9318975

Cited by 2 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Var w (10) The variance both within and between groups has an impact on the F-value, which quantifies the degree of differentiation.…”

Section: Anova F = Var Bmentioning

confidence: 99%

“…With a handwriting recognition system, handwriting features are captured and analyzed through various inputs, including touch screens, electronic pens, images, pen tablets, scanners, and paper documents. Every person has a distinct handwriting style that makes it a compelling subject of study in user identification [8] and useful for various applications, such as personal identification [9], [10], pattern recognition, digital forensics, questioned document examination, criminalistics, fingerprint analysis, signature verification [11], [12], and biometric analysis [13].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Hasan,

Rahim,

Shin

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Handwriting is controlled by neurons in the brain's nervous system, reflecting an individual's personality and psychology. This unique characteristic can be used for various applications, including user authentication, assessment of neurodegenerative disorders, and classification of handedness, gender, and age groups. Traditional authentication systems require memorization, information leakage, and fingerprints, making them vulnerable to security breaches. The majority of researchers have studied the limitations of image quality, camera frames, and light effects on text and image-dependent performance. Therefore, this paper mainly focused on real-time, text-independent handwriting fine-motor data and proposed an efficient authentication system with low cost using efficient feature extraction and optimal feature selection approaches. This research utilizes two benchmark databases, including the handwriting data of 48 (24+24) participants collected via a sensor-based pen tablet. Each participant wrote the 10 words five times repeatedly, making it a total of 2400 samples. The handwriting classification of the different individuals is in 3 phases: feature extraction, feature selection, and classification. A total of 91 features (statistical, kinematic, spatial, and composite) were extracted from more accurate, real-time numerical handwriting data. The efficient and optimal features have been selected using four feature selection approaches, namely, Pearson's r correlation, ANOVA-F, Mutual Information Gain, and PCA, among which the ANOVA-F test and PCA perform well for handwriting-extracted data. Then, 14 machine learning (ML) models and 7 deep learning (DL) models were applied to handle the problem of individual classification, with both no-and full-feature-selection scenarios considered. The experimental analysis has been conducted with different angles and perspectives, such as K-Fold cross-validation, testing system efficiency considering 5/10/15/24/48 individuals, and in the case of individual tasks. It shows that ML-based algorithms, namely, CATBOOST (99.07%) with ANOVA-F and DL-based models, namely, BiLSTM (98.31%) with PCA-selected features, provide the highest accuracy with dataset 2, among others that advocate the practicality and reliability of choosing this system for user identification.

show abstract

“…Var w (10) The variance both within and between groups has an impact on the F-value, which quantifies the degree of differentiation.…”

Section: Anova F = Var Bmentioning

confidence: 99%

mentioning

confidence: 99%

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Hasan,

Rahim,

Shin

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Every individual has his or her own individuality in handwriting style. This reality is exercised in various applications like signature verification [1,2] or person identification [3,4]. Personal identification based on handwriting is a unique approach that has a variety of potential applications like security purposes, financial activities, forensic activities, demography-based writer authentication, and archaeological authentication (e.g., to identify ancient document writers).…”

Section: Introductionmentioning

confidence: 99%

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

et al. 2021

Self Cite

View full text Add to dashboard Cite

Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.

show abstract

User Authentication Through Pen Tablet Data Using Imputation and Flatten Function

Cited by 2 publications

References 3 publications

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

Contact Info

Product

Resources

About