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Information 2018, 9(10), 261; https://doi.org/10.3390/info9100261

An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image

School of Physics and Electronics, Shandong Normal University, Jinan 250000, China
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Received: 22 September 2018 / Revised: 15 October 2018 / Accepted: 16 October 2018 / Published: 22 October 2018
(This article belongs to the Special Issue Machine Learning on Scientific Data and Information)
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Abstract

The human face as a biometric trait has been widely used for personal identity verification but it is still a challenging task under uncontrolled conditions. With the development of hyper-spectral imaging acquisition technology, spectral properties with sufficient discriminative information bring new opportunities for a facial image process. This paper presents a novel ensemble method for skin feature segmentation of a hyper-spectral facial image based on a k-means algorithm and a spanning forest algorithm, which exploit both spectral and spatial discriminative features. According to the closed skin area, local features are selected for further facial image analysis. We present the experimental results of the proposed algorithm on various public face databases which achieve higher segmentation rates. View Full-Text
Keywords: hyper-spectral imaging; band selection; clustering ensemble; k-means; spatial-spectral classification; minimum spanning forest hyper-spectral imaging; band selection; clustering ensemble; k-means; spatial-spectral classification; minimum spanning forest
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Zhao, Y.; Wu, M.; Zhang, L.; Wang, J.; Wei, D. An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image. Information 2018, 9, 261.

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