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Independent Component Analysis (ICA) has found its application in face recognition successfully. The goals are to estimate the components from raw image data. These components are then used to extract features of face images on which face classification is conducted. The components play key role in face recognition system. However these separated components are not equally important in terms of contribution...
Independent component analysis (ICA) has found its application in face recognition successfully. In practice several ICA representations can be derived. Particularly they include spatial ICA, spatiotemporal ICA, and localized spatiotemporal ICA, which respectively extract features of face images in terms of space domain, time-space domain, and local region. Our work has shown that while spatiotemporal...
In this paper, we proposed a joint spatial and temporal ICA method for face recognition, and compared the performances of different ICA approaches (spatiotemporal ICA and spatial ICA). In our study, two face datasets collected by AcSys FRS discovery system were used. One face dataset involves less variation in terms of face expression and head movement, while the other encompasses much more change...
Micro-motions, such as vibrations or rotations of an object or structures on the object, induce additional frequency modulations on returned radar signal, which generates sidebands about the object's Doppler frequency, called micro-Doppler by V.C. Chen et al. (2002). In this paper, we investigated statistical classification methods for target classification using their micro-Doppler signatures. At...
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