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Yasir Salih Ali
Researcher at Umm al-Qura University
Publications - 10
Citations - 198
Yasir Salih Ali is an academic researcher from Umm al-Qura University. The author has contributed to research in topics: Ballistocardiography & Motion estimation. The author has an hindex of 5, co-authored 10 publications receiving 131 citations. Previous affiliations of Yasir Salih Ali include Universiti Teknologi Petronas.
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Journal ArticleDOI
Heart rate estimation using facial video: A review
Mohamed Abul Hassan,Mohamed Abul Hassan,Aamir Saeed Malik,David Fofi,Naufal M. Saad,Babak Karasfi,Yasir Salih Ali,Fabrice Meriaudeau +7 more
TL;DR: A critical review of digital camera based heart rate estimating method on facial skin is presented, which showed the reliability of the state of the art methods and provided direction to improve for situations involving illumination variance and motion variance.
Journal ArticleDOI
Video-Based Heartbeat Rate Measuring Method Using Ballistocardiography
Mohamed Abul Hassan,Aamir Saeed Malik,David Fofi,Naufal Mohamed Saad,Yasir Salih Ali,Fabrice Meriaudeau +5 more
TL;DR: A ballistocardiography model based on Newtons third law of force and dynamics of harmonic oscillation is proposed to measure the rigid involuntary head motion caused by the ejection of the blood from the heart to overcome problems related to illumination and motion variance.
Proceedings ArticleDOI
Optimal source selection for image photoplethysmography
TL;DR: An optimal selection on the region of interest (ROI) and color spaces to extract photoplethysmography signals from facial videos showed that the ROI selection onThe forehead and the green spectrum of the additive color space to provides higher accuracy for heart rate measurement.
Journal ArticleDOI
Motion estimation of high density crowd using fluid dynamics
Muhammad Umer Farooq,Mohamed Naufal Bin Mohamed Saad,Aamir Saeed Malik,Yasir Salih Ali,Sultan Daud Khan +4 more
TL;DR: This paper focuses on state-of-the-art fluid dynamics ME methods developed over the last one and the half-decade for high-density crowd analysis and discusses the strengths and weaknesses and viability of FD ME methods for anomaly detection at high crowd densities.
Journal ArticleDOI
Sparse Representation for Crowd Attributes Recognition
Aliyu Nuhu Shuaibu,Ibrahima Faye,Yasir Salih Ali,Nidal Kamel,Mohd Naufal Saad,Aamir Saeed Malik +5 more
TL;DR: Quantitative evaluation indicates that the proposed model display superior accuracy, precision, and recall in classifying human behaviors with linear support vector machine when compared with the state-of-the-art methods.