Coupling deep and handcrafted features to assess smile genuineness
This work addresses the need for accurate and efficient smile genuineness assessment in facial expression recognition, but it is incremental as it builds on existing feature-based and deep learning approaches.
The paper tackled the problem of assessing smile genuineness from video sequences by combining deep learning features from an LSTM network with handcrafted features for facial action unit dynamics, resulting in a solution that outperforms baseline techniques and operates in real-time.
Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time.