Coupling deep and handcrafted features to assess smile genuineness

Benedykt Pawlus; Bogdan Smolka; Jolanta Kawulok; Michal Kawulok

doi:10.1117/12.3013815

7 June 2024 Coupling deep and handcrafted features to assess smile genuineness

Benedykt Pawlus, Bogdan Smolka, Jolanta Kawulok, Michal Kawulok

Proceedings Volume 13034, Real-Time Image Processing and Deep Learning 2024; 1303406 (2024) https://doi.org/10.1117/12.3013815
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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.

Conference Presentation

Citation Download Citation

Benedykt Pawlus, Bogdan Smolka, Jolanta Kawulok, and Michal Kawulok "Coupling deep and handcrafted features to assess smile genuineness", Proc. SPIE 13034, Real-Time Image Processing and Deep Learning 2024, 1303406 (7 June 2024); https://doi.org/10.1117/12.3013815

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