Autoencoder: Approach to the reduction of the dimension of the vector space with controlled loss of information

Akinin, Maxim V.; Akinina, Natalya V.; Taganov, Alexandr I.; Nikiforov, Michael B.

doi:10.1109/meco.2015.7181894

Cited by 3 publications

(2 citation statements)

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“…Machine learning techniques have revolutionized many fields of science including computer vision, pattern recognition, and speech processing through its powerful ability to learn nonlinear relationships over hidden layers, which makes it suitable for automatic features learning and modeling of nonlinear transformations. Deep neural networks (DNNs) can be used for feature extraction as well as for dimensionality reduction [7], [9]. A large number of classification techniques has been used for FER.…”

Section: Fig 1: Facial Expression Recognition (Fer) Block Diagrammentioning

confidence: 99%

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Using deep autoencoders for facial expression recognition

Usman

Latif

Qadir

2017

2017 13th International Conference on Emerging Technologies (ICET)

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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-theart feature selection and dimension reduction techniques.

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Section: Fig 1: Facial Expression Recognition (Fer) Block Diagrammentioning

confidence: 99%

“…Features descriptors like histograms of oriented gradients (HOG) [4], Local Gabor features [5] and Weber Local Descriptor (WLD) [6] are widely used techniques for FER, whereas HOG has shown to be particularly effective in literature for the task of FER [7]. The dimensionality of these features is usually high.…”

Section: Introductionmentioning

confidence: 99%