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|Title: ||A Support Vector Data Description Committee for Face Detection|
|Authors: ||Yi-Hung Liu;Yung Ting;Shian-Shing Shyu;Chang-Kuo Chen;Chung-Lin Lee;Mu-Der Jeng|
|Issue Date: ||2016-08-04T01:25:07Z
|Publisher: ||Mathematical Problems in Engineering|
|Abstract: ||Abstract: Face detection is a crucial prestage for face recognition and is often treated as a binary (face and nonface) classification problem.
While this strategy is simple to implement, face detection accuracy would drop when nonface training patterns are undersampled.
To avoid these problems, we propose in this paper a one-class learning-based face detector called support vector data description
(SVDD) committee, which consists of several SVDD members, each of which is trained on a subset of face patterns. Nonfaces are
not required in the training of the SVDD committee. Therefore, the face detection accuracy of SVDD committee is independent of
the nonface training patterns. Moreover, the proposed SVDD committee is also able to improve generalization ability of the original
SVDD when the face data set has a multicluster distribution. Experiments carried out on the extended MIT face data set show that
the proposed SVDD committee can achieve better face detection accuracy than the widely used SVM face detector and performs
better than other one-class classifiers, including the original SVDD and the kernel principal component analysis (Kernel PCA).
|Appears in Collections:||[電機工程學系] 期刊論文|
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