  This paper presents our approach for the engagement intensity regression task
of EmotiW 2019. The task is to predict the engagement intensity value of a
student when he or she is watching an online MOOCs video in various conditions.
Based on our winner solution last year, we mainly explore head features and
body features with a bootstrap strategy and two novel loss functions in this
paper. We maintain the framework of multi-instance learning with long
short-term memory (LSTM) network, and make three contributions. First, besides
of the gaze and head pose features, we explore facial landmark features in our
framework. Second, inspired by the fact that engagement intensity can be ranked
in values, we design a rank loss as a regularization which enforces a distance
margin between the features of distant category pairs and adjacent category
pairs. Third, we use the classical bootstrap aggregation method to perform
model ensemble which randomly samples a certain training data by several times
and then averages the model predictions. We evaluate the performance of our
method and discuss the influence of each part on the validation dataset. Our
methods finally win 3rd place with MSE of 0.0626 on the testing set.
