Synopsis
Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this research, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms competitive algorithms in terms of of accuracy and computational complexity.
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About the Author
Dr. Rehab F. Abdel-Kader received the Ph.D. degree in Computer Science and Engineering from Auburn University in 2003. She is currently an Associate Professor at the Electrical Engineering Department, Faculty of Engineering, Port-Said University. Research interests include Artificial Intelligence and Computer Vision.
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An Efficient Eye Detection and Tracking System based on Particle Swarm
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this research, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms competitive algorithms in terms of of accuracy and computational complexity. 60 pp. Englisch. Seller Inventory # 9783330001237
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abdel-Kader RehabDr. Rehab F. Abdel-Kader received the Ph.D. degree in Computer Science and Engineering from Auburn University in 2003. She is currently an Associate Professor at the Electrical Engineering Department, Facul. Seller Inventory # 158852847
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this research, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms competitive algorithms in terms of of accuracy and computational complexity.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Seller Inventory # 9783330001237
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this research, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms competitive algorithms in terms of of accuracy and computational complexity. Seller Inventory # 9783330001237
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Taschenbuch. Condition: Neu. An Efficient Eye Detection and Tracking System based on Particle Swarm | Rehab Abdel-Kader (u. a.) | Taschenbuch | 60 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330001237 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 108217935
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