Color channel-wise recurrent learning for facial expression recognition

Jang, Jongsoo; Kim, Dae Hoe; Kim, Hyung-Il; Ro, Yong Man

doi:10.1109/icassp.2017.7952353

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Cited by 5 publications

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“…They tested this technique on several datasets and obtained higher accuracy than state-of-the-art techniques. Jang et al [26] worked on color images and attained 85.74% recognition rate by using color channel-wise recurrent learning using deep learning. Similarly, Talele et al [27] used LBP features and ANN for classification and the maximum success rate was 95.48%.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%