CNN as a feature extractor in gaze recognition
Arun Gopal Govindaswamy, Enid N. H. Montague, Daniela Stan Raicu, Jacob David Furst · 2020
In this paper, we employ a Convolutional Neural Network (CNN) in predicting physician gaze. This paper focuses on two aspects – one comparison between hand-crafted features and CNN-based learned features, and two in investigating the impact of fully-connected layers in an end-to-end CNN model. The pre-trained CNN model based on VGG16 through transfer learning is used as a feature extractor and a K-Nearest Neighbor and a Random Forest (RF) algorithm were used as the classifier of physician gaze. The CNN-RF and CNN–K-NN models were compared with the traditional end-to-end CNN model and through a series of experiments and statistical tests of significance, we show that the power of CNN comes from the features extraction part and that the fully connected layers of the CNN have comparable performance to the random forest and the k-NN classifiers. We also show that the CNN-based learned features provide substantial distinguishable power in classifying physician gaze.