CS-3DLBP and geometry based person independent 3D facial action unit detection
Face is the key component in understanding emotions which play significant roles in many areas from security and entertainment to psychology and education. In this paper, we propose a method to detect facial action units in 3D face data by combining novel geometric properties and a new descriptor based on the Local Binary Pattern (LBP) methodology. The proposed method enables person and gender independent facial action unit detection. The decision level fusion is used by employing the Random Forests classifiers to combine geometric and LBP based features. Unlike the previous methods which suffer from the diversity among different persons and normalize features utilizing neutral faces, our method extracts features on a single 3D face data. Besides, we show that orientation based 3D LBP descriptor can be implemented efficiently in terms of size and time without degrading the performance. We tested our method on the Bosphorus database and present comparative results with the existing methods. Our results outperform those of existing methods, achieving a mean receiver operating characteristic area under curve of 97.7%.