Extraction of Useful Features from Neural Network for Facial Expression Recognition
Naoki Imamura, Hiroki Nomiya, Teruhisa Hochin · 2019
Facial expression intensity has been proposed to digitize the degree of facial expressions in order to retrieve impressive scenes from lifelog videos. However, correlation of facial features compared to each facial expression is not determined objectively. Therefore, we recognize facial expressions by using a neural network and calculate the contribution score of input toward output. We verify the score correctly by comparing the accuracy transitions depending on reducing useful and useless features, and process the score statistically. As a result, we extract useful facial features from the neural network.