2015
DOI: 10.1016/j.robot.2014.12.003
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Hybrid learning model and MMSVM classification for on-line visual imitation of a human with 3-D motions

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Cited by 8 publications
(8 citation statements)
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“…In addition, no occlusion image is considered in [39]. On the other hand, the SVM with suitable kernel function can deal with the non‐linear separation classification problem such that a better performance can be achieved when the face recognition is in the presence of pose, illumination changes, or occlusion [23, 34, 35].…”
Section: Resultsmentioning
confidence: 99%
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“…In addition, no occlusion image is considered in [39]. On the other hand, the SVM with suitable kernel function can deal with the non‐linear separation classification problem such that a better performance can be achieved when the face recognition is in the presence of pose, illumination changes, or occlusion [23, 34, 35].…”
Section: Resultsmentioning
confidence: 99%
“…To establish the identity of a given probe face image, a multiclass classifier such as SVM [29][30][31][32][33][34], nearest neighbour, and RDFs can be used. Owing to the flexibility in the choice of the form of the threshold, the robustness toward a small number of data points, its unique solution and usage in almost any classification problem with the right kernel, MSVM is adopted for the classification of different faces.…”
Section: Classification Of Multiclassmentioning
confidence: 99%
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“…The moving of body limb causes the position of COP or COP point or PCOM point to move, there is a rule to adjust the body. To the purpose of the full-body imitation, the body balance includes not only the lower body but the upper body [24]. The rule of adjustment is separated into two part, called the upper body (UB) and the lower body (LB).…”
Section: Action Spacementioning
confidence: 99%