An efficient multimodal 2D + 3D feature-based approach to automatic facial expression recognition

• Published in: Computer vision and image understanding Vol. 140; pp. 83 - 92
• Main Authors: Li, Huibin, Ding, Huaxiong, Huang, Di, Wang, Yunhong, Zhao, Xi, Morvan, Jean-Marie, Chen, Liming
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2015; Elsevier
• Subjects: Computer Science; Computer Vision and Pattern Recognition; Facial expression recognition; Image Processing; Local shape descriptor; Local texture descriptor; Multimedia; Multimodal fusion; Neural and Evolutionary Computing; Signal and Image Processing; Local texture descriptor; Facial expression recognition; Local shape descriptor; Multimodal fusion
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2015.07.005

•We propose a feature-based 2D+3D multimodal facial expression recognition method.•It is fully automatic benefit from a large set of automatically detected landmarks.•The complementarities between 2D and 3D features are comprehensively demonstrated.•Our method achieves the best accuracy on the BU–3DFE database so far.•A good generalization ability is shown on the Bosphorus database. We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU–3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar–CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU–3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32%. Moreover, a good generalization ability is shown on the Bosphorus database.