Recognition Using Dee Networks
نویسندگان
چکیده
We develop a technique using deep for human facial expression recognition. Images of preprocessed with photometric normalization manipulation to remove illumination variance. F then extracted by convolving each preprocessed Gabor filters. Kernel PCA is applied to feature them into the deep neural network that consists o hidden layers and a softmax classifier. The deep n using greedy layer-wise strategy. We use the Kanade Dataset for training and testing. Reco performed on six basic expressions (i.e. surpri anger, happiness, sadness). To test the ro classification system further, and for benchmark add a seventh emotion, namely “contempt” recognition tests. We construct confusion matrix performance of the deep network. It is demon network generalizes to new images fairly succ average recognition rate of 96.8% for six emotion seven emotions. In comparison with shallower neu SVM methods, the proposed deep network met better recognition performance. Keywords—Multi-layer neural network; emo Gabor filters; Kernel principal component analysis.
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