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.
Yao al @cite_3 applied the scattering operator @cite_4 over 4D data, producing geometrical and textual scattering representations. Multiple Kernel Learning (MKL) was then applied to learn from this information for FER. Authors in @cite_31 presented a statistical shape model with global and local constraints in an attempt to recognize FEs. They claimed that the combination of global face shape and local shape index-based information can be handy for FER. Li al @cite_17 introduced a Dynamic Geometrical Image Network (DGIN) for automatically recognizing expressions. Given 4D data, the differential geometry quantities are estimated first, followed by generating geometrical images. These images are then fed into the DGIN for an end-to-end training. The prediction results are based on fusing the predicted scores of different types of geometrical images.
@cite_31 @cite_4 @cite_3 @cite_17
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This paper presents a novel approach to face recognition, named Multi-resolution Histograms of Local Variation Patterns (MHLVP), in which face images are represented as the concatenation of the local spatial histogram of local variation patterns computed from the multi-resolution Gabor features. For a face image with abundant texture and shape information, aGabor feature map(GFM) is computed by convolving the image with each of the forty multi-scale and multi-orientation Gabor filters. Each GFM is then divided into small non-overlapping regions to enhance its shape information, and then Local Binary Pattern (LBP) histograms are extracted for each region and concatenated into a feature histogram to enhance the texture information in the specific GFM. Further more, all of the feature histograms extracted from the forty GFMs are further concatenated into a single feature histogram as the final representation of the given face image. Eventually, the identification is achieved by histogram intersection operation. Our experimental results on FERET face databases show that the proposed method performs terrifically better than the performance of some classical results including the best results in FERET'97. In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol. Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder–decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods. Facial Expression Recognition (FER) is one of the most important topics in the domain of computer vision and pattern recognition, and it has attracted increasing attention for its scientific challenges and application potentials. In this article, we propose a novel and effective approach to FER using multi-model two-dimensional (2D) and 3D videos, which encodes both static and dynamic clues by scattering convolution network. First, a shape-based detection method is introduced to locate the start and the end of an expression in videos; segment its onset, apex, and offset states; and sample the important frames for emotion analysis. Second, the frames in Apex of 2D videos are represented by scattering, conveying static texture details. Those of 3D videos are processed in a similar way, but to highlight static shape details, several geometric maps in terms of multiple order differential quantities, i.e., Normal Maps and Shape Index Maps, are generated as the input of scattering, instead of original smooth facial surfaces. Third, the average of neighboring samples centred at each key texture frame or shape map in Onset is computed, and the scattering features extracted from all the average samples of 2D and 3D videos are then concatenated to capture dynamic texture and shape cues, respectively. Finally, Multiple Kernel Learning is adopted to combine the features in the 2D and 3D modalities and compute similarities to predict the expression label. Thanks to the scattering descriptor, the proposed approach not only encodes distinct local texture and shape variations of different expressions as by several milestone operators, such as SIFT, HOG, and so on, but also captures subtle information hidden in high frequencies in both channels, which is quite crucial to better distinguish expressions that are easily confused. The validation is conducted on the BU-4DFE and BP-4D databa ses, and the accuracies reached are very competitive, indicating its competency for this issue.