text: Recognizing facial expressions is one of the central problems in computer vision. Temporal image sequences have useful spatio-temporal features for recognizing expressions. In this paper, we propose a new 3D Convolution Neural Network (CNN) that can be trained end-to-end for facial expression recognition on temporal image sequences without using facial landmarks. More specifically, a novel 3D convolutional layer that we call Local Binary Volume (LBV) layer is proposed. The LBV layer, when used with our newly proposed LBVCNN network, achieve comparable results compared to state-of-the-art landmark-based or without landmark-based models on image sequences from CK+, Oulu-CASIA, and UNBC McMaster shoulder pain datasets. Furthermore, our LBV layer reduces the number of trainable parameters by a significant amount when compared to a conventional 3D convolutional layer. As a matter of fact, when compared to a 3x3x3 conventional 3D convolutional layer, the LBV layer uses 27 times less trainable parameters.
  @cite_36: Temporal information has useful features for recognizing facial expressions. However, to manually design useful features requires a lot of effort. In this paper, to reduce this effort, a deep learning technique, which is regarded as a tool to automatically extract useful features from raw data, is adopted. Our deep network is based on two different models. The first deep network extracts temporal appearance features from image sequences, while the other deep network extracts temporal geometry features from temporal facial landmark points. These two models are combined using a new integration method in order to boost the performance of the facial expression recognition. Through several experiments, we show that the two models cooperate with each other. As a result, we achieve superior performance to other state-of-the-art methods in the CK+ and Oulu-CASIA databases. Furthermore, we show that our new integration method gives more accurate results than traditional methods, such as a weighted summation and a feature concatenation method.
  @cite_4: One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. In this paper, we propose a part-based hierarchical bidirectional recurrent neural network (PHRNN) to analyze the facial expression information of temporal sequences. Our PHRNN models facial morphological variations and dynamical evolution of expressions, which is effective to extract “temporal features” based on facial landmarks (geometry information) from consecutive frames. Meanwhile, in order to complement the still appearance information, a multi-signal convolutional neural network (MSCNN) is proposed to extract “spatial features” from still frames. We use both recognition and verification signals as supervision to calculate different loss functions, which are helpful to increase the variations of different expressions and reduce the differences among identical expressions. This deep evolutional spatial-temporal network (composed of PHRNN and MSCNN) extracts the partial-whole, geometry-appearance, and dynamic-still information, effectively boosting the performance of facial expression recognition. Experimental results show that this method largely outperforms the state-of-the-art ones. On three widely used facial expression databases (CK+, Oulu-CASIA, and MMI), our method reduces the error rates of the previous best ones by 45.5 , 25.8 , and 24.4 , respectively.
  @cite_7: Expressions are facial activities invoked by sets of muscle motions, which would give rise to large variations in appearance mainly around facial parts. Therefore, for visual-based expression analysis, localizing the action parts and encoding them effectively become two essential but challenging problems. To take them into account jointly for expression analysis, in this paper, we propose to adapt 3D Convolutional Neural Networks (3D CNN) with deformable action parts constraints. Specifically, we incorporate a deformable parts learning component into the 3D CNN framework, which can detect specific facial action parts under the structured spatial constraints, and obtain the discriminative part-based representation simultaneously. The proposed method is evaluated on two posed expression datasets, CK+, MMI, and a spontaneous dataset FERA. We show that, besides achieving state-of-the-art expression recognition accuracy, our method also enjoys the intuitive appeal that the part detection map can desirably encode the mid-level semantics of different facial action parts.
summary: Deep learning-based models have achieved state-of-the-art results in facial expression recognition. Liu applied 3D CNN with deformable action part constraints (3D CNNDAP) to the problem of expression recognition @cite_7 . Recent models use geometric features like facial landmarks to further boost the accuracy. Jung proposed two separate networks called DTAN and DTGN and jointly fine-tuned the two networks to achieve state-of-the-art performance @cite_36 . The DTAN network is a simple 3D convolutional network that captures spatio-temporal information from temporal image sequences. The DTGN network is a fully-connected network that captures temporal variations in facial landmarks. Guo improved Jung 's result and trained a spatial network (MSCNN) and a temporal network (PHRNN) separately and jointly fine-tuned them @cite_4 . MSCNN is a simple convolutional network on peak expression images. PHRNN is a collection of subnets (recurrent neural networks) that are connected in a binary tree-like structure. Facial landmarks are divided into four parts and passed at the bottom of this structure and the outputs of the subnets are concatenated at the next layer. The process is repeated for upper layers and the final layer is a softmax classification layer.