مشاهده مشخصات مقاله
Tensor-Based Face Representation and Recognition Using Multi-Linear Subspace Analysis
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نویسنده (ها) |
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Hadis Mohseni
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Shohreh Kasaei
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مربوط به کنفرانس |
چهاردهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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چکیده |
Discriminative subspace analysis is a popular approach
for a variety of applications. There is a growing
interest in subspace learning techniques for face
recognition. Principal component analysis (PCA) and
eigenfaces are two important subspace analysis methods
have been widely applied in a variety of areas.
However, the excessive dimension of data space often
causes the curse of dimensionality dilemma, expensive
computational cost, and sometimes the singularity
problem. In this paper, a new supervised discriminative
subspace analysis is presented by encoding face
image as a high order general tensor. As face space
can be considered as a nonlinear submanifold embedded
in the tensor space, a decomposition method called
Tucker tensor is used which can effectively decomposes
this sparse space. The performance of the proposed
method is compared with that of eigenface, Fisherface,
tensor LPP, and ORO4×2 on ORL and Weizermann
databases. Conducted experimental results show the
superiority of the proposed method. |
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قیمت |
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برای اعضای سایت : ۱٠٠,٠٠٠ ریال
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برای دانشجویان عضو انجمن : ۲٠,٠٠٠ ریال
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برای اعضای عادی انجمن : ۴٠,٠٠٠ ریال
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