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WIREs Data Mining Knowl Discov
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Facial feature discovery for ethnicity recognition

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The salient facial feature discovery is one of the important research tasks in ethnical group face recognition. In this paper, we first construct an ethnical group face dataset including Chinese Uyghur, Tibetan, and Korean. Then, we show that the effective sparse sensing approach to general face recognition is not working anymore for ethnical group facial recognition if the features based on whole face image are used. This is partially due to a fact that each ethnical group may have its own characteristics manifesting only in specified face regions. Therefore, we will analyze the particularity of three ethnical groups and aim to find the common characterizations in some local regions for the three ethnical groups. For this purpose, we first use the facial landmark detector STASM to find some important landmarks in a face image, then, we use the well‐known data mining technique, the mRMR algorithm, to select the salient geometric length features based on all possible lines connected by any two landmarks. Second, based on these selected salient features, we construct three “T” regions in a face image for ethnical feature representation and prove them to be effective areas for ethnicity recognition. Finally, some extensive experiments are conducted and the results reveal that the proposed “T” regions with extracted features are quite effective for ethnical group facial recognition when the L2‐norm is adopted using the sparse sensing approach. In comparison to face recognition, the proposed three “T” regions are evaluated on the olivetti research laboratory face dataset, and the results show that the constructed “T” regions for ethnicity recognition are not suitable for general face recognition. This article is categorized under: Algorithmic Development > Structure Discovery Algorithmic Development > Biological Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Classification
The order of attribute identification in face recognition
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Use of the olivetti research laboratory database for testing
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Olivetti research laboratory face dataset
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The results of classifiers
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The software for face ethnic analysis
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The accuracy based on L2 norm
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The accuracy based on L1 norm
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The accuracy based on L0 norm
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The image coding of “T” region
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Facial feature region of various weights
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The various weight of length facial features
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Landmarks obtained using STASM
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The results of face image using single scale retinex
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A part of face dataset
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Data capture environment
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The living area distribution of three ethnicities
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Technologies > Classification
Fundamental Concepts of Data and Knowledge > Knowledge Representation
Algorithmic Development > Biological Data Mining
Algorithmic Development > Structure Discovery

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