Transcript Face recognition using improved local texture pattern.pptx

Face recognition using improved local
texture pattern
Speaker: Wei-Lung Chang
Date:2011/12/30
Authors: W.K. Yang and C.Y. Sun
Source: 2011 9th World Congress on Intelligent
Control and Automation (WCICA)
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Outline
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Introduction
Local binary patterns (LBP)
Local ternary patterns (LTP)
Their work
Experiments
Conclusion
Comment
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Introduction(1/2)
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Local texture patterns based methods have been
widely used in face recognition. LBP and LTP
are two typical feature descriptor methods.
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So they present an improved LBP method by
replacing the central pixel with the average of
the region, LTP too.
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Introduction(2/2)
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The experimental results on ORL, AR face
databases show that our present methods have
better performance than LBP and LTP.
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A key issue in face recognition is to find
effective descriptors for face appearance.
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There are two main approaches: holistic
methods and local descriptor methods
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Local binary patterns (LBP)(1/2)
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Local binary patterns (LBP)(2/2)
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Local ternary patterns (LTP)(1/3)
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Local ternary patterns (LTP)(2/3)
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Local ternary patterns (LTP)(3/3)
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For simplicity, each ternary patters is split into
positive and negative part. They will be then
combined in the final step of computation.
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Their work (1/5)
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Their work(2/5)
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Their work(3/5)
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Their work(4/5)
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As aforesaid, the face recognition algorithm can
be described as follows:
◦ Step1. We calculate improved LBP and improved LTP
on the image and get the code image.
◦ Step2. We divide the code images into m*n subregions.
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Their work(5/5)
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Experiments(1/4)
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They do experiments on ORL and AR databases
to evaluate the performance of the improved
methods.
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To evaluate the robustness of their proposed
method against the noise, the add Gaussian
noise on the ORL face database.
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Experiments(2/4)
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Experiments(3/4)
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Experiments(4/4)
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The AR data based contains over 4000 color
face image of 126 people.
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Conclusion
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In this paper, they present an improve LBP and
an improve LTP for face recognition. But it is
difficult to set a suitable threshold in LTP.
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The experiments show that our present methods
are more robust to the noise and illumination
variations etc. than LBP and LTP.
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Comment
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這一篇提出的ILBP跟ILTP，為了減少雜訊的
影響，但在臉部變識的成功機率並沒有比原
來的方法還要好很多。
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因為LBP跟LTP運用在人臉辨識上有十分快速
的優點，所以我想把它運用在我之後想做的
東西上面。
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