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Indian Journal of Data Communication and Networking, 2022
Human faces are identified and localized via facial detection, which ignores any backdrop objects like curtains, windows, trees, etc. Each frame of the video is run through multiple stages of classifiers in OpenCV's Harr cascade, and if the frame passes through each level, the face is considered to be present; otherwise, the frame is dismissed from the
Jufin P A, Amrutha N
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Human faces are identified and localized via facial detection, which ignores any backdrop objects like curtains, windows, trees, etc. Each frame of the video is run through multiple stages of classifiers in OpenCV's Harr cascade, and if the frame passes through each level, the face is considered to be present; otherwise, the frame is dismissed from the
Jufin P A, Amrutha N
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2009
When we want to analyze the continuous change of the face in an image sequence, applying face tracking methods is a better choice than applying the face detection methods to each image frame. Usually, the face tracking methods are more efficient than the ordinary face detection methods because they can utilize the trajectory of the face in the previous
Daijin Kim, Jaewon Sung
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When we want to analyze the continuous change of the face in an image sequence, applying face tracking methods is a better choice than applying the face detection methods to each image frame. Usually, the face tracking methods are more efficient than the ordinary face detection methods because they can utilize the trajectory of the face in the previous
Daijin Kim, Jaewon Sung
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Face as Mouse Through Visual Face Tracking
The 2nd Canadian Conference on Computer and Robot Vision (CRV'05), 2005This paper introduces a novel camera mouse driven by visual face tracking based on a 3D model. As the camera becomes standard configuration for personal computers (PCs) and computation speed increases, achieving human-machine interaction through visual face tracking becomes a feasible solution to hands-free control. Human facial movements can be broken
Jilin Tu, Hai Tao, Thomas Huang
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Angry faces are tracked more easily than neutral faces during multiple identity tracking
Cognition and Emotion, 2017We investigated whether and how emotional facial expressions affect sustained attention in face tracking. In a multiple-identity and object tracking paradigm, participants tracked multiple target faces that continuously moved around together with several distractor faces, and subsequently reported where each target face had moved to.
Jie Li +3 more
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2009 International Conference on Measuring Technology and Mechatronics Automation, 2009
The Camshift algorithm fails to track face easily while it is occluded, so a new face tracking method is proposed in this paper. This method combines the Camshift algorithm and the GM(1,1) model with optimized background values. By using moving vector information, this method can effectively track face even occluded by other static objects. The GM(1,1)
Jianxiong Tang, Jianxin Zhang
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The Camshift algorithm fails to track face easily while it is occluded, so a new face tracking method is proposed in this paper. This method combines the Camshift algorithm and the GM(1,1) model with optimized background values. By using moving vector information, this method can effectively track face even occluded by other static objects. The GM(1,1)
Jianxiong Tang, Jianxin Zhang
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Automatic face feature tracking
Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030), 2003A reliable face tracking can be obtained by integrating of the region detection, feature locating and tracking. To detect the face feature region, we used the mean shift method that is a clustering technique for multivariate data, and to track face landmark features, we also used the STK tracking method.
null Kyung-Chan Jin, null Jin-Ho Cho
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Face verification through tracking facial features
Journal of the Optical Society of America A, 2001We propose an algorithm for face verification through tracking facial features by using sequential importance sampling. Specifically, we first formulate tracking as a Bayesian inference problem and propose to use Markov chain Monte Carlo techniques for obtaining an empirical solution.
B, Li, R, Chellappa
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Face retrieval in face track using sparse representation
2018 International Workshop on Advanced Image Technology (IWAIT), 2018One popular approach for working on video datasets is to use face tracks [1, 2], face sequence containing faces of one person, for matching instead of single face as in static image datasets. In this article, we propose face track retrieval using sparse representation. At first, every image of face tracks is normalized to uniform size. Then, feature is
Shinfeng D. Lin +2 more
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A face tracking method using feature point tracking
2012 International Conference on Information Security and Intelligent Control, 2012In the paper, we proposed a face tracking method using feature point tracking. The proposed face tracking method is composed of two parts: face detection and face tracking. To evaluate the proposed method, several videos containing different kinds of face movement are captured by using low-cost webcam. Experimental results show that our proposed method
Guo-Shiang Lin, Tung-Sheng Tsai
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Eye Tracking Unconscious Face-to-Face Confrontations
Psychological Science, 2011In primates, dominance/submission relationships are generally automatically and nonaggressively established in face-to-face confrontations. Researchers have argued that this process involves an explicit psychological stress-manipulation mechanism: Striding with a threatening expression, while keeping direct eye contact, outstresses rivals so that they
Terburg, D. +4 more
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