Font Size: Facial Expression Recognition for User Response Information Extraction
Genki Yamaguchi, Naoya Yamaguchi, Kenji Matsui · 2017
This paper describes our preliminary study of facial expression recognition in order to extract user response information. We used Kinect to recognize facial expressions of the user in real time, we extract 6 facial expression categories (neutral, happiness, disgust, surprise, sadness, closed-eye). As for the recognition process, we applied a multi-layer-perceptron to classify the face expressions. A total of 1,912 facial expression data sets were collected from 17 subjects. We performed holdout test using 80% of training data and 20% of test data. The recognition rate without “sadness” feature was around 90%, and the rate using every categories was around 80%. Then, we focused on detecting the 0 to 5 level of “smile” face, such as “smiling”→“laughing”→“loud laugh”. The experimental result showed that the average correct answer rate was 61.3%. However, when considering up to ± 1 error adjustment, the correct answer rate was the average of 93.5%.