The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Facial Expression Recognition Using Constructive Feedforward Neural Networks
Abstract
A new technique for facial expression recognition is proposed, which uses the two-dimensional (2D) discrete cosine transform (DCT) over the entire face image as a feature detector and a constructive one-hidden-layer feedforward neural network as a facial expression classifier. An input-side pruning technique, proposed previously by the authors, is also incorporated into the constructive learning process to reduce the network size without sacrificing the performance of the resulting network. The proposed technique is applied to a database consisting of images of 60 men, each having five facial expression images (neutral, smile, anger, sadness, and surprise). Images of 40 men are used for network training, and the remaining images of 20 men are used for generalization and testing. Confusion matrices calculated in both network training and generalization for four facial expressions (smile, anger, sadness, and surprise) are used to evaluate the performance of the trained network. It is demonstrated that the best recognition rates are 100% and 93.75% (without rejection), for the training and generalizing images, respectively. Furthermore, the input-side weights of the constructed network are reduced by approximately 30% using our pruning method. In comparison with the fixed structure back propagation-based recognition methods in the literature, the proposed technique constructs one-hidden-layer feedforward neural network with fewer number of hidden units and weights, while simulta…
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“First, the neural network-based methods are found to be particularly promising for facial expression recognition, since the neural networks can easily implement the mapping from the feature space of face images to the facial expression space [24].”
From Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition (2017) · cites this paper as [24]
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Kotsia, I., & Pitas, I. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.884954 · Cited by 687
Zhi, R., Flierl, M., Ruan, Q., & Kleijn, W. B. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2044788 · Cited by 260
Facial Expression Recognition with Identity and Emotion Joint Learning
Li, M., Xu, H., Huang, X., Song, Z., Liu, X., & Li, X. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2880201 · Cited by 128
Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition
Huang, Y., Yang, J., Liao, P., & Pan, J. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/2107451 · Cited by 115
Metadata
- Authors
- Ma, L., & Khorasani, K.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 2004-06
- Volume
- 34
- Issue
- 3
- Pages
- 1588-1595
- ISSN
- 1083-4419
- Cited by
- 160
- References
- 2
- Language
- en
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- Ma, L., & Khorasani, K. (2004). Facial Expression Recognition Using Constructive Feedforward Neural Networks. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). https://doi.org/10.1109/tsmcb.2004.825930