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Hyperspectral imaging provides new opportunities for improving face recognition accuracy. However, it poses such challenges as difficulty in data acquisition, low signal to noise ratio (SNR), and high dimensionality. In this paper, we propose a novel method for hyperspectral face recognition with good recognition rates. We first reduce noise adaptively from each spectral band and then crop each face...
Conventional power systems have been experiencing transition from centralized supply side management to decentralized supply&demand side management due to power system restructuring and addition of distributed generations (DGs) and Smart Grids (SGs) or Smart Distribution Systems (SDSs) with renewable power sources in the past two decades. Therefore load management under the new operating environment...
Conventional power systems have been experiencing transition from centralized supply side management to decentralized supply&demand side management due to power system restructuring and addition of distributed generations (DGs) and Smart Grids (SGs) or Smart Distribution Systems (SDSs) with renewable power sources in the past two decades. Therefore load management under the new operating environment...
A new general dimension reduction framework based on similar and dissimilar metric learning is proposed in this paper which allows us to exploit the geometry of data to reduce the data dimension for classification and visualization. The general formulation can unify the existing dimension reduction algorithms within a common framework. Furthermore, this metric learning framework can be used as a general...
Face recognition has received significant attention in the last decades for many potential applications. Recently, the scale invariant feature transform (SIFT) becomes an interesting technique for the task of object recognition. This paper investigated the application of the SIFT approach to the face recognition and proposed a new method based on SIFT and support vector machine (SVM) for the face...
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