2021 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), 2021
This work compares face gesture recognition methods based on deep learning convolutional neural n... more This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the combination of both databases (Jaffe & CK+) over a five-fold cross-validation strategy. The DCNN4, DCNN2, and DCNN+Autoencoder models achieved best performance mean accuracy scores of 95%, 94%, and 96% for the Jaffe, CK+, and Jaffe & CK+ databases, respectively. Moreover, according to the cross-entropy loss function, the selected models did not incur overfitting.
2021 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), 2021
This work compares face gesture recognition methods based on deep learning convolutional neural n... more This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the combination of both databases (Jaffe & CK+) over a five-fold cross-validation strategy. The DCNN4, DCNN2, and DCNN+Autoencoder models achieved best performance mean accuracy scores of 95%, 94%, and 96% for the Jaffe, CK+, and Jaffe & CK+ databases, respectively. Moreover, according to the cross-entropy loss function, the selected models did not incur overfitting.
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