A survey of human-computer interaction (HCI) &amp; natural habits-based behavioural biometric modalities for user recognition schemes

Gupta, Sandeep; Maple, Carsten; Crispo, Bruno; Raja, Kiran B.; Yautsiukhin, Artsiom; Martinelli, Fabio

doi:10.1016/j.patcog.2023.109453

Cited by 15 publications

(4 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the integration of machine learning with Behavioral analytics will create a powerful synergy in fraud detection. Behavioral biometrics, for instance, will become more re ned, enabling nancial institutions to identify users based on unique Behavioral patterns, such as typing speed, mouse movements, and touchscreen interactions (Gupta et al, 2023). This enhanced level of user authentication will strengthen security measures and make it more di cult for fraudsters to deceive traditional authentication methods.…”

Section: Advancements In Machine Learning and Behavioral Analyticsmentioning

confidence: 99%

Fraud Detection in Fintech Leveraging Machine Learning and Behavioral Analytics

Josyula

2023

Preprint

View full text Add to dashboard Cite

Fraud detection in the fintech sector is a critical area of concern as financial transactions increasingly shift to digital platforms. This paper presents a comprehensive analysis of enhancing fraud detection in fintech by combining machine learning techniques, leveraging behavioral analytics, and adopting RegTech solutions. The objective is to develop a holistic approach that strengthens fraud prevention strategies, ensures regulatory compliance, and safeguards the interests of customers and financial institutions. The paper begins with an introduction that sets the context by highlighting the growing importance of fraud detection in the digital financial landscape. It outlines the research objectives, scope, and structure of the paper. Subsequently, the methodology section details the data collection process, the selection and comparative analysis of machine learning models, the integration of behavioral analytics, and the implementation of RegTech solutions. The paper concludes with a summary of findings and contributions, emphasizing the significance of adopting a holistic approach to fraud detection in the fintech industry. It underscores the need for financial institutions to embrace advanced technologies, comply with data privacy regulations, and collaborate within the industry to combat financial crimes effectively.

show abstract

Section: Advancements In Machine Learning and Behavioral Analyticsmentioning

confidence: 99%

Fraud Detection in Fintech Leveraging Machine Learning and Behavioral Analytics

Josyula

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Keystroke dynamics is a soft biometric, which is widely used in many applications such as user authentication [1][2][3], fraud detection [4], biometric identification [5,6] and human-computer interaction [7]. Keystroke dynamics refers to the habitual patterns or rhythms an individual exhibits while typing on a keyboard input device.…”

Section: Introductionmentioning

confidence: 99%

KD-Net: Continuous-Keystroke-Dynamics-Based Human Identification from RGB-D Image Sequences

Dai,

Zhao,

et al. 2023

Sensors

View full text Add to dashboard Cite

Keystroke dynamics is a soft biometric based on the assumption that humans always type in uniquely characteristic manners. Previous works mainly focused on analyzing the key press or release events. Unlike these methods, we explored a novel visual modality of keystroke dynamics for human identification using a single RGB-D sensor. In order to verify this idea, we created a dataset dubbed KD-MultiModal, which contains 243.2 K frames of RGB images and depth images, obtained by recording a video of hand typing with a single RGB-D sensor. The dataset comprises RGB-D image sequences of 20 subjects (10 males and 10 females) typing sentences, and each subject typed around 20 sentences. In the task, only the hand and keyboard region contributed to the person identification, so we also propose methods of extracting Regions of Interest (RoIs) for each type of data. Unlike the data of the key press or release, our dataset not only captures the velocity of pressing and releasing different keys and the typing style of specific keys or combinations of keys, but also contains rich information on the hand shape and posture. To verify the validity of our proposed data, we adopted deep neural networks to learn distinguishing features from different data representations, including RGB-KD-Net, D-KD-Net, and RGBD-KD-Net. Simultaneously, the sequence of point clouds also can be obtained from depth images given the intrinsic parameters of the RGB-D sensor, so we also studied the performance of human identification based on the point clouds. Extensive experimental results showed that our idea works and the performance of the proposed method based on RGB-D images is the best, which achieved 99.44% accuracy based on the unseen real-world data. To inspire more researchers and facilitate relevant studies, the proposed dataset will be publicly accessible together with the publication of this paper.

show abstract

“…It is urgently necessary to create natural, comfortable, and effective natural interaction technologies in order to circumvent and eliminate the limitations of these regulations. Instead of mechanically converting their operating intentions into specific instructions that the machine can understand, operators can express their willingness to interact naturally as if they were speaking to people thanks to natural interaction technology [1]. This is in contrast to the traditional method of interaction.…”

Section: Introductionmentioning

confidence: 99%

The Comfort and Measurement Precision-Based Multi-Objective Optimization Method for Gesture Interaction

Wang,

Hou,

Tian

et al. 2023

Bioengineering

View full text Add to dashboard Cite

As an advanced interaction mode, gestures have been widely used for human–computer interaction (HCI). This paper proposes a multi-objective optimization method based on the objective function JCP to solve the inconsistency between the gesture comfort JCS and measurement precision JPH in the gesture interaction. The proposed comfort model CS takes seventeen muscles and six degrees of freedom into consideration based on the data from muscles and joints, and is capable of simulating the energy expenditure of the gesture motion. The CS can provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. The measurement precision model ∆PH is calculated from the measurement error (∆XH,∆YH,∆ZH) caused by calibration, that provides a means to evaluate the efficiency of the gesture interaction. The modeling and simulation are implemented to analyze the effectiveness of the multi-objective optimization method proposed in this paper. According to the result of the comparison between the objective function JCS, based on the comfort model CS, and the objective function JPH, based on the measurement precision models ∆PH, the consistency and the difference can be found due to the variation of the radius rB_RHO and the center coordinates PB_RHOxB_RHO,yB_RHO,zB_RHO. The proposed objective function JCP compromises the inconsistency between the objective function JCS and JPH. Therefore, the multi-objective optimization method proposed in this paper is applied to the gesture design to improve the ergonomics and operation efficiency of the gesture, and the effectiveness is verified through usability testing.

show abstract

A survey of human-computer interaction (HCI) & natural habits-based behavioural biometric modalities for user recognition schemes

Cited by 15 publications

References 68 publications

Fraud Detection in Fintech Leveraging Machine Learning and Behavioral Analytics

Fraud Detection in Fintech Leveraging Machine Learning and Behavioral Analytics

KD-Net: Continuous-Keystroke-Dynamics-Based Human Identification from RGB-D Image Sequences

The Comfort and Measurement Precision-Based Multi-Objective Optimization Method for Gesture Interaction

Contact Info

Product

Resources

About