Description: In this paper, we propose a two-phase test sample
representation method for face recognition. The first phase of
the proposed method seeks to represent the test sample as
a linear combination of all the training samples and exploits
the representation ability of each training sample to determine
M “nearest neighbors” for the test sample. The second phase
represents the test sample as a linear combination of the
determined M nearest neighbors and uses the representation
result to perform classification. We propose this method with the
following assumption: the test sample and its some neighbors
are probably from the same class. Thus, we use the first phase
to detect the training samples that are far from the test sample
and assume that these samples have no effects on the ultimate
classification decision. This is helpful to accurately classify the
test sample. We will also show the probability explanation of
the proposed method. A number of face recognition experiments
show that our method performs very well. Platform: |
Size: 460458 |
Author:may@uestc.edu.cn |
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