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The experimental results show that the proposed method can construct a robust FER system whose results outperform well-known state-of-the-art FER methods.
The adoption of cosine dissimilarity and the Frobenius norm significantly enhance the performance of the FER system.
A nonholistic representation and a low rank approximation of the data make better performance for an FER system [16].
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FER systems are typically divided into two categories: FER systems based on FACS and FER systems that use other methods for face analysis.
The proposed approach shows promising results for AU classification in multi-view FER systems.
Neural networks have also been used in FER systems, specifically Long Short-Term-Memory Recurrent Neural Networks (LSTM-RMM) [15].
Moreover, these AUs are part of the standard set used in the majority of FER systems based on FACS [40 42].
In the following paragraphs, we present the state-of-the-art in these two major research areas, FER systems and personality trait prediction systems, focusing on the latter as it is more relevant to our current work.
Regarding FER systems using other face analysis methods for predicting emotions, we mention the use of Local Directional Pattern LDPP) features [12] extracted from time-sequential depth videos, augmented using optical flows, and classified through Generalized Discriminant Analysis (GDA).
The interaction of CO with Ca-FER was investigated by a combination of IR spectroscopy and microcalorimetry and by periodic DFT calculations employing the ω/r correlation for calculations of CO stretching frequencies in order to gain more insight on Ca2+ cation coordination in the FER zeolite and site-specificity of CO stretching frequency in IR spectra of CO/Ca-FER system.
It can be observed that in all scenarios SIMO/MIMO transceivers outperform (both in terms of BER and FER) SISO systems.
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CEO of Professional Science Editing for Scientists @ prosciediting.com