Learning Spatio-temporal Features with Partial Expression Sequences for on-the-Fly Prediction
This addresses the need for real-time prediction in interactive settings like affective agents, where delays are intolerable, representing a domain-specific incremental improvement.
The paper tackled the problem of delayed facial expression prediction in video sequences by proposing a spatio-temporal feature learning method that enables on-the-fly prediction with partial sequences, achieving higher recognition rates compared to state-of-the-art methods on two datasets.
Spatio-temporal feature encoding is essential for encoding facial expression dynamics in video sequences. At test time, most spatio-temporal encoding methods assume that a temporally segmented sequence is fed to a learned model, which could require the prediction to wait until the full sequence is available to an auxiliary task that performs the temporal segmentation. This causes a delay in predicting the expression. In an interactive setting, such as affective interactive agents, such delay in the prediction could not be tolerated. Therefore, training a model that can accurately predict the facial expression "on-the-fly" (as they are fed to the system) is essential. In this paper, we propose a new spatio-temporal feature learning method, which would allow prediction with partial sequences. As such, the prediction could be performed on-the-fly. The proposed method utilizes an estimated expression intensity to generate dense labels, which are used to regulate the prediction model training with a novel objective function. As results, the learned spatio-temporal features can robustly predict the expression with partial (incomplete) expression sequences, on-the-fly. Experimental results showed that the proposed method achieved higher recognition rates compared to the state-of-the-art methods on both datasets. More importantly, the results verified that the proposed method improved the prediction frames with partial expression sequence inputs.