Recently, some sparsecoding methods with geometrical constraint have been proposed, in which local geometrical structure of the data points was preserved during sparsecoding process. These methods have been applied ...
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ISBN:
(纸本)9781479919611
Recently, some sparsecoding methods with geometrical constraint have been proposed, in which local geometrical structure of the data points was preserved during sparsecoding process. These methods have been applied to classification problems and gained much success. However, they failed to use label information which has been proved to be useful in supervised sparsecoding and discriminant manifold learning. In this paper, we propose a discriminant sparse coding approach with geometrical constraint. Labels are used to learn an intrinsic graph and a penalty graph, and these graphs are then embedded into sparsecoding framework as constraints. The local geometric structure within each class is preserved and the separability between different classes is enforced. As a result, the discrimination of sparsecoding will be improved. Experiments on benchmark databases demonstrate the effectiveness of the proposed method.
sparse representation based classification (SRC) has attracted much attention in face analysis such as face recognition (FR) and face expression recognition (FER). Currently, most of SRC based methods treated face as ...
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ISBN:
(纸本)9781479906529
sparse representation based classification (SRC) has attracted much attention in face analysis such as face recognition (FR) and face expression recognition (FER). Currently, most of SRC based methods treated face as a whole component which results in under-utilization of the complementary in different facial parts. In this paper, we present an approach which can effectively explore the complementary of different facial parts to boost the performance of face analysis. In particular, we employ multi-view sparsecoding techniques to learn the factorized representation of different facial components. Furthermore, we incorporate label information into the objective function to enforce the discriminability. To evaluate the performance, we conduct face analysis experiments including FR and FER on JAFFE database. Experimental results demonstrate that the proposed method can significantly boost the performance of face analysis.
sparse representation based classification (SRC) has attracted much attention in face analysis such as face recognition (FR) and face expression recognition (FER). Currently, most of SRC based methods treated face as ...
详细信息
ISBN:
(纸本)9781479906505
sparse representation based classification (SRC) has attracted much attention in face analysis such as face recognition (FR) and face expression recognition (FER). Currently, most of SRC based methods treated face as a whole component which results in under-utilization of the complementary in different facial parts. In this paper, we present an approach which can effectively explore the complementary of different facial parts to boost the performance of face analysis. In particular, we employ multi-view sparsecoding techniques to learn the factorized representation of different facial components. Furthermore, we incorporate label information into the objective function to enforce the discriminability. To evaluate the performance, we conduct face analysis experiments including FR and FER on JAFFE database. Experimental results demonstrate that the proposed method can significantly boost the performance of face analysis.
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