High dimensional local binary patterns for facial expression recognition in the wild
Krystian Radlak, Bogdan Smołka · 2016
In this paper, we propose an automatic method of facial expressions recognition in static images using the high dimensional Local Binary Patterns (LBP). In this research some existing algorithms for face detection, facial landmarks localization, face normalization and recognition were combined and adopted for facial expressions classification. The novelty of the contribution lies in the application of the Random Frog algorithm used in the gene selection in the microarray experiments together with the high dimensional LBP as features of the Support Vector Machines (SVM) classifier used for the facial expressions classification. The proposed method was evaluated on the Static Facial Expressions in the Wild (SFEW) database, which contains the face images coming from movies and is very similar to real world scenarios. The performed tests show that the high dimensional LBP and features calculated from the regions around facial landmarks can achieve significant improvements over the state-of-the-art methods.