Using Deep Autoencoders for Facial Expression Recognition
Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature. Selecting the most important features is a very crucial task for systems like facial expression recognition. This paper investigates the performance of deep autoencoders for feature selection and dimension reduction for facial expression recognition on multiple levels of hidden layers. The features extracted from the stacked autoencoder outperformed when compared to other state-of-the-art feature selection and dimension reduction techniques.
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Dimensionality ReductionFacial Expression RecognitionFacial Expression Recognition (FER)feature selectionMethods 이 논문이 사용한 방법론
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