This paper proposes a novel 4D Facial Expression Recognition (FER) method using Collaborative Cross-domain Dynamic Image Network (CCDN). Given a 4D data of face scans, we first compute its geometrical images, and then combine their correlated information in the proposed cross-domain image representations. The acquired set is then used to generate cross-domain dynamic images (CDI) via rank pooling that encapsulates facial deformations over time in terms of a single image. For the training phase, these CDIs are fed into an end-to-end deep learning model, and the resultant predictions collaborate over multi-views for performance gain in expression classification. Furthermore, we propose a 4D augmentation scheme that not only expands the training data scale but also introduces significant facial muscle movement patterns to improve the FER performance. Results from extensive experiments on the commonly used BU-4DFE dataset under widely adopted settings show that our proposed method outperforms the state-of-the-art 4D FER methods by achieving an accuracy of 96.5 indicating its effectiveness.
Research in the areas of 3-D face recognition and 3-D facial expression analysis has intensified in recent years. However, most research has been focused on 3-D static data analysis. In this paper, we investigate the facial analysis problem using dynamic 3-D face model sequences. One of the major obstacles for analyzing such data is the lack of correspondences of features due to the variable number of vertices across individual models or 3-D model sequences. In this paper, we present an effective approach for establishing vertex correspondences using a tracking-model-based approach for vertex registration, coarse-to-fine model adaptation, and vertex motion trajectory (called vertex flow) estimation. We propose to establish correspondences across frame models based on a 2-D intermediary, which is generated using conformal mapping and a generic model adaptation algorithm. Based on our newly created 3-D dynamic face database, we also propose to use a spatiotemporal hidden Markov model (ST-HMM) that incorporates 3-D surface feature characterization to learn the spatial and temporal information of faces. The advantage of using 3-D dynamic data for face recognition has been evaluated by comparing our approach to three conventional approaches: 2-D-video-based temporal HMM model, conventional 2-D-texture-based approach (e.g., Gabor-wavelet-based approach), and static 3-D-model-based approaches. To further evaluate the usefulness of vertex flow and the adapted model, we have also applied a spatial-temporal face model descriptor for facial expression classification based on dynamic 3-D model sequences. Automatic facial expression recognition on 3D face data is still a challenging problem. In this paper we propose a novel approach to perform expression recognition automatically and flexibly by combining a Bayesian Belief Net (BBN) and Statistical facial feature models (SFAM). A novel BBN is designed for the specific problem with our proposed parameter computing method. By learning global variations in face landmark configuration (morphology) and local ones in terms of texture and shape around landmarks, morphable Statistic Facial feature Model (SFAM) allows not only to perform an automatic landmarking but also to compute the belief to feed the BBN. Tested on the public 3D face expression database BU-3DFE, our automatic approach allows to recognize expressions successfully, reaching an average recognition rate over 82 . In this paper, we propose a fully automatic approach for person-independent 3D facial expression recognition. In order to extract discriminative expression features, each aligned 3D facial surface is compactly represented as multiple global histograms of local normal patterns from multiple normal components and multiple binary encoding scales, namely Multi-Scale Local Normal Patterns (MS-LNPs). 3D facial expression recognition is finally carried out by modeling multiple kernel learning (MKL) to efficiently embed and combine these histogram based features. By using the SimpleMKL algorithm with the chi-square kernel, we achieved an average recognition rate of 80.14 based on a fair experimental setup. To the best of our knowledge, our method outperforms most of the state-of-the-art ones.  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. 