Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features ext...
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ISBN:
(纸本)9781479914821
Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representationbased action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowest-rank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.
A fully automatic framework is proposed for 3D face recognition and its performance is justified by the FRGC database. 3D facial representation extracted by the Dual-tree Complex Wavelet Transform (DT-CWT) is introduc...
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ISBN:
(纸本)9781467321969
A fully automatic framework is proposed for 3D face recognition and its performance is justified by the FRGC database. 3D facial representation extracted by the Dual-tree Complex Wavelet Transform (DT-CWT) is introduced to reflect the facial geometry properties. Low redundancy makes it more effective and efficient to describe the discriminant feature in 2.5D range data. In this paper, DT-CWT is used in conjunction with Weighted Spherical Face representation to form a rejection classifier, which quickly eliminates a large number of candidate gallery faces. The remaining faces are then verified utilizing sparse representation based classification. Our method achieves the verification rate of 98.86% on All vs. All experiment at an FAR of 0.1%.
We consider the problem of automatically recognizing a human face from its multi-view images with unconstrained poses and illuminations. We formulate the multi-view face recognition problem as that of classifying amon...
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ISBN:
(纸本)9781457713033
We consider the problem of automatically recognizing a human face from its multi-view images with unconstrained poses and illuminations. We formulate the multi-view face recognition problem as that of classifying among several multi-input (views) regression models by using a novel joint dynamic sparserepresentation method which exploits jointly the inter-correlation among all the multi-view images in order to make a decision. Extensive experiments on CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method.
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