![](bg7.png)
verification mechanism. Without any complex ma-
chine learning approach, the designed mechanism
verifies people using only the LBP-Faces and its sim-
ilarity distances. Although the stage of unsupervised
clustering need to take a period of time to process, the
new feature can immediately generated by the LBP-
Faces in the stage of testing. The new feature is cal-
culated only by the similarity distance, which means
the computation complexity is very low and can speed
up the verification procedure. Our proposed method
can automatically derive the LBP-Faces and verify-
ing people in nearly real-time, which is applicable in
intelligent mobile phone and embedded system de-
sign. Experimental results show that our method can
achieve higher recognition accuracy than that of the
LBP and Eigenface in the Labeled Faces in the Wild
(LFW). Even though the recognition of our method
might be less promising when the face is partially oc-
cluded or the head pose is severely varied, we believe
that the improvement can be achieved by utilizing the
3D information to enhance the LBP-Faces and clus-
tering the LBP-Faces into different poses. In conclu-
sion, this work is a good initial start, which prove the
reasonableness of our novel idea in face verification
and still have a long way to go for future stronger
work.
In this work, we just choose K-means as default
unsupervised learning algorithm, so in the future, we
will firstly attempt on more clustering mechanisms.
In addition, we will take 3D scenario into consider-
ation in unsupervised learning stage, that is, we will
derive LBP-Faces according to different poses to im-
prove the accuracy. Moreover, the data set—for ex-
ample, the size and the sampling images, used in
learning stage can be also further researched on.
ACKNOWLEDGEMENTS
The work was supported in part by the Image and Vi-
sion Lab at National Taiwan University. The author
was supported by MediaTek Fellowship.
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