This paper presents an approach for generating photorealistic video sequences of dynamically varying facial expressions in human-agent interactions. To this end, we study human-human interactions to model the relationship and influence of one individual's facial expressions in the reaction of the other. We introduce a two level optimization of generative adversarial models, wherein the first stage generates a dynamically varying sequence of the agent's face sketch conditioned on facial expression features derived from the interacting human partner. This serves as an intermediate representation, which is used to condition a second stage generative model to synthesize high-quality video of the agent face. Our approach uses a novel L1 regularization term computed from layer features of the discriminator, which are integrated with the generator objective in the GAN model. Session constraints are also imposed on video frame generation to ensure appearance consistency between consecutive frames. We demonstrated that our model is effective at generating visually compelling facial expressions. Moreover, we quantitatively showed that agent facial expressions in the generated video clips reflect valid emotional reactions to behavior of the human partner.

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely positive-semidefinite constrained metric learning (PCML) and nonnegative-coefficient constrained metric learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification, and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

In this paper, we proposed a bi-stage framework for image-based emotion recognition by combining the advantages of deep convolutional neural networks (D-CNN) and hypergraphs. To exploit the representational power of D-CNN, we remodeled its last hidden feature layer as the `attribute' layer in which each hidden unit produces probabilities on a specific semantic attribute. To describe the high-order relationship among facial images, each face was assigned to various hyperedges according to the computed probabilities on different D-CNN attributes. In this way, we tackled the emotion prediction problem by a transductive learning approach, which tends to assign the same label to faces that share many incidental hyperedges (attributes), with the constraints that predicted labels of training samples should be similar to their ground truth labels. We compared the proposed approach to state-of-the-art methods and its effectiveness was demonstrated by extensive experimentation.

In this paper, we proposed a novel framework for facial expression recognition, in which face images were taken as vertices in a hypergraph and the task of expression recognition was formulated as the problem of hypergraph based inference. A hybrid strategy was developed to construct hyperedges: we generated probabilities of facial action units by deep convolutional networks and took each action unit as an ‘attribute’ to represent a hyperedge; we also formed hyperedges by using embedded network features before the last full connected layer to perform local clustering. In this way, each face image was assigned to various hyperedges by exploiting the representational power of deep convolutional networks. Our facial expression recognition system generates expression labels by a hypergraph based transductive inference approach, which tends to assign the same label to vertices that share many incidental hyperedges, with the constraints that predicted labels of training images should be similar to their ground truth labels. We compared the proposed approach to state-of-the-art methods and its effectiveness was demonstrated by extensive experimentation.

We present results from a pilot study that explored evidence of non-conscious facial expression mirroring exhibited by human dyads in face-to-face interactions. We captured video data of study participants engaged in an online collaborative activity on science topics. The data was sub-sampled and organized into two sets: one consisting of time-synchronized video clips from dyad pairs and the other from unrelated nominal-dyads (or non-dyads). The videos were analyzed with an automated facial expression approach and distance between two video clips of each pair (from both two sets) was computed using a temporal pyramid matching strategy. By employing a two-sample t-test on these two populations, our results demonstrate a statistically significant convergence or mirroring of facial expressions between dyad pairs, which holds over a wide range of model parameter settings and extensive experimentation.

We present a framework for unsupervised image categorization in which images containing specific objects are taken as vertices in a hypergraph and the task of image clustering is formulated as the problem of hypergraph partition. First, a novel method is proposed to select the region of interest (ROI) of each image, and then hyperedges are constructed based on shape and appearance features extracted from the ROIs. Each vertex (image) and its k-nearest neighbors (based on shape or appearance descriptors) form two kinds of hyperedges. The weight of a hyperedge is computed as the sum of the pairwise affinities within the hyperedge. Through all of the hyperedges, not only the local grouping relationships among the images are described, but also the merits of the shape and appearance characteristics are integrated together to enhance the clustering performance. Finally, a generalized spectral clustering technique is used to solve the hypergraph partition problem. We compare the proposed method to several methods and its effectiveness is demonstrated by extensive experiments on three image databases.

This paper presents a component-based deformable model for generalized face alignment, in which a novel bistage statistical model is proposed to account for both local and global shape characteristics. Instead of using statistical analysis on the entire shape, we build separate Gaussian models for shape components to preserve more detailed local shape deformations. In each model of components, a Markov network is integrated to provide simple geometry constraints for our search strategy. In order to make a better description of the nonlinear interrelationships over shape components, the Gaussian process latent variable model is adopted to obtain enough control of shape variations. In addition, we adopt an illumination-robust feature to lead the local fitting of every shape point when light conditions change dramatically. To further boost the accuracy and efficiency of our component-based algorithm, an efficient subwindow search technique is adopted to detect components and to provide better initializations for shape components. Based on this approach, our system can generate accurate shape alignment results not only for images with exaggerated expressions and slight shading variation but also for images with occlusion and heavy shadows, which are rarely reported in previous work.

In this paper, we propose a new transductive learning framework for image retrieval, in which images are taken as vertices in a weighted hypergraph and the task of image search is formulated as the problem of hypergraph ranking. Based on the similarity matrix computed from various feature descriptors, we take each image as a `centroid' vertex and form a hyperedge by a centroid and its k-nearest neighbors. To further exploit the correlation information among images, we propose a probabilistic hypergraph, which assigns each vertex vito a hyperedge ejin a probabilistic way. In the incidence structure of a probabilistic hypergraph, we describe both the higher order grouping information and the affinity relationship between vertices within each hy-peredge. After feedback images are provided, our retrieval system ranks image labels by a transductive inference approach, which tends to assign the same label to vertices that share many incidental hyperedges, with the constraints that predicted labels of feedback images should be similar to their initial labels. We compare the proposed method to several other methods and its effectiveness is demonstrated by extensive experiments on Corel5K, the Scene dataset and Caltech 101.

Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus keyword similarity is modeled in the annotation framework. Numerous experiments are designed to compare the performance between features, feature combinations and regularization based feature selection methods applied on the image annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group sparsity based method is more accurate and stable than others.

In this paper, we present a new framework of video object segmentation, in which we formulate the task of extracting prominent objects from a scene as the problem of hypergraph cut. We initially over-segment each frame in the sequence, and take the over-segmented image patches as the vertices in the graph. Different from the traditional pairwise graph structure, we build a novel graph structure, hypergraph, to represent the complex spatio-temporal neighborhood relationship among the patches. We assign each patch with several attributes that are computed from the optical flow and the appearance-based motion profile, and the vertices with the same attribute value is connected by a hyperedge. Through all the hyperedges, not only the complex non-pairwise relationships between the patches are described, but also their merits are integrated together organically. The task of video object segmentation is equivalent to the hypergraph partition, which can be solved by the hypergraph cut algorithm. The effectiveness of the proposed method is demonstrated by extensive experiments on nature scenes.

This paper presents a component based deformable model for generalized face alignment, in which a novel bi-stage statistical framework is proposed to account for both local and global shape characteristics. Instead of using statistical analysis on the entire shape as in previous alignment work, we build separate Gaussian models for shape components to preserve more detailed local shape deformations. In each model of components the Markov Network is integrated to provide simple geometry constraints for our search strategy. In order to make a better description of the nonlinear interrelationships over the shape components, the Gaussian process latent variable model is adopted to obtain enough control of full range shape variations. Furthermore, we propose an illumination-robust feature to lead the local fitting of every shape point when light conditions change dramatically. Based on this approach, our system can generate optimal shape for images with exaggerated expressions and under variable illumination, as evidenced by extensive experimentation.

Previous 2-D face alignment algorithms are generally quite sensitive to illumination variation and poor initialization. To account for these two obstacles, two forms of relatively lighting invariant descriptors - intrinsic gray-level information and intrinsic edge information - rare adopted in our algorithm to direct shape search. The former is recovered from local intensity normalization and useful at localizing face contours accurately despite its dependency on initialization. The latter is extracted from normalized local regions by Canny edge filtering and is robust at coarse alignment in spite of poor initialization. The different merits of these two forms of intrinsic information motivate us to employ them at different stages of our face alignment process. Extensive experimentations show that this proposed approach allows our system to handle not only illumination variation, but also poor initialization.