Coupling deep and handcrafted features to assess smile genuineness
Journal:
arXiv
Published Date:
Mar 20, 2025
Abstract
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.
