Robust dynamic facial expressions recognition using Lbp-Top descriptors and Bag-of-Words classification model

Resumo

In this work we investigate the problem of robust dynamic facial expression recognition. We develop a complete pipeline that relies on the LBP-TOP descriptors and the Bag-of-Words (BoW) model for basic expressions classification. Experiments performed on the standard dataset such as the Extended Cohn-Kanade (CK+) database show that the developed approach achieves the average recognition rate of 97.7%, thus outperforming state-of-the-art methods in terms of accuracy. The proposed method is quite robust as it uses only relevant parts of video frames such as areas around mouth, noise, eyes, etc. Ability to work with arbitrary length sequence is also a plus for practical applications, since it means there is no need for complex temporal normalization methods.