Facial Spoof Detection Using Support Vector Machine

Das, Tandra Rani; Hasan, Sharad; Sarwar, Shifat; Das, Jugal Krishna; Rahman, Muhammad Arifur

doi:10.1007/978-981-33-4673-4_50

Cited by 18 publications

(3 citation statements)

References 15 publications

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“…These findings reveal that the SVM approach using transformed geometric-based features with Euclidean distance can distinctively differentiate attention from inattention. This outcome is similar to the findings in [74], where the authors compare SVM and CNN in facial spoof detection. The SVM achieved an accuracy of 91%, outperforming the CNN approach, which only achieved 76.31%.…”

Section: Discussionsupporting

confidence: 87%

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

Banire

Al-Thani

Qaraqe

et al. 2021

J Healthc Inform Res

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Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.

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Section: Discussionsupporting

confidence: 87%

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

Banire

Al-Thani

Qaraqe

et al. 2021

J Healthc Inform Res

View full text Add to dashboard Cite

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“…There is a vast array of works of machine learning in diverse area i.e. Classification of Fake News [4], Facial Spoof Detection [5], Image classification [6], Auditory attention state [7], Computational biology [8], Trust management for IOT [9], Text processing [10,11] are going.…”

Section: Literature Reviewmentioning

confidence: 99%

A Recommender System for Adaptive Examination Preparation using Pearson Correlation Collaborative Filtering

Hossain,

Tasnim,

Hoque

et al. 2021

Int J Auto AI Mach learn

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Distance learning is any type of far-off instruction where the understudy isn't actually present for the exercise. It is blasting gratitude to the force of the Internet. Distance learning plays a vital role for examination preparation where multiple choice questions can be utilized to evaluate the performance of students. Multiple Choice Question (MCQ) is a type of question used in the examination to evaluate the performance of students accordingly where usually four options are given along with the question, and one has to choose the correct answer. This research includes a simulation model that has been built to keep the learners continue to learn the subjects they might be weak in. We have developed a methodology that may guide a student to update his/her area of weakness by using a recommender system based on Pearson Correlation Collaborative Filtering approach. The paper describes a recommender system that will keep track of a learner's profile and create an adaptive training mechanism using the performance matrix.

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“…With the advent of technology, this problem can be facilitated by data driven models introduced by machine learning. Machine learning approaches can train itself from a large historical dataset and outcome a most efficient prediction in solving the issue [4][5][6][7]. This data-driven and robust solution for parking availability prediction can guide the drivers, thereby reducing traffic congestion and the time cost.…”

Section: Introductionmentioning

confidence: 99%

Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

Sadia,

Reza,

Alam

et al. 2021

Int J Auto AI Mach learn

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Drivers spend an enormous amount of time searching for parking spots every year. Waste of time, emission of carbon and air pollution have been issues in hunting for parking spots without proper prediction. In this paper, we have proposed to build a framework based on Recurrent Neural Network (RNN) using Long Short Term Memory (LSTM) and Random Forest Regression model to provide prediction of parking availability and compared results afterwards. A real-world case of parking spots availability consisting of 5,500 parking spots in Kuala Lumpur City Centre (KLCC), Malaysia, has been used for regression implementation in this comparative analysis. The results showed that random forest outperformed LSTM approach based on performance metrics.

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Facial Spoof Detection Using Support Vector Machine

Cited by 18 publications

References 15 publications

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

A Recommender System for Adaptive Examination Preparation using Pearson Correlation Collaborative Filtering

Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

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