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WIREs Data Mining Knowl Discov
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Eyebrow semantic description via clustering based on Axiomatic Fuzzy Set

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In this paper, we aim to extract the eyebrow semantic descriptors based on the Axiomatic Fuzzy Set (AFS) theory. First, we normalize the image of the eyebrows and automatically mark it by using a recently proposed facial landmarks detector. Second, a recent clustering algorithm based on AFS theory for eyes semantics abstraction is used to cluster these detected eyebrow landmarks and give semantic descriptors for each eyebrow. Finally, BU‐4DFE and Multi‐PIE databases are used to validate the effectiveness of the proposed approach. Furthermore, the eyebrow descriptions with different expressions and similar expressions are investigated and we show that the semantic descriptors are closely related to expressions. The experimental results show that the eyebrow semantic concepts obtained by the AFS clustering algorithm are better than the results produced by the traditional clustering methods (k‐means and FCM) in terms of consistency for different expressions.

This article is categorized under:

  • Fundamental Concepts of Data and Knowledge > Knowledge Representation
  • Algorithmic Development > Biological Data Mining
The flow chart of extracting the semantic facial descriptor
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Two images on different level of angry expression
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Number of clusters and fuzzy cluster validity index υ α for clustering right eyebrow
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The feature representation of the right eyes: (a) height of right eye; (b) width of right eyebrow
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The feature extraction of right eyebrow: (a) perimeter of right eyebrow; (b) height of right eyebrow; (c) centroid distance of right eyebrow; (d) area of right eyebrow; (e) width of right eyebrow. O: Centroid of right eyebrow
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Fundamental Concepts of Data and Knowledge > Knowledge Representation
Algorithmic Development > Biological Data Mining

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