For improving the nonlinear alignment performance of Active Appearance Models (AAM), we apply a variant of the nonlinear manifold learning algorithm, Local Linear Embedded, to model shape-texture manifold. Experiments show that our method maintains a lower alignment residual to some small scale movements compared with traditional AAM based on Principal Component Analysis (PCA) and makes a successful alignment to large scale motions when PCA-AAM failed.

The manner of a person's eye movement conveys much about nonverbal information and emotional intent beyond speech. This paper describes work on expressing emotion through eye behaviors in virtual agents based on the parameters selected from the AU-Coded facial expression database and real-time eye movement data (pupil size, blink rate and saccade). A rule-based approach to generate primary (joyful, sad, angry, afraid, disgusted and surprise) and intermediate emotions (emotions that can be represented as the mixture of two primary emotions) utilized the MPEG4 FAPs (facial animation parameters) is introduced. Meanwhile, based on our research, a scripting tool, named EEMML (Emotional Eye Movement Markup Language) that enables authors to describe and generate emotional eye movement of virtual agents, is proposed.

In this paper, we propose a new method that employs two novel features, correlation density (Cd) and fractal dimension (Fd), to recognize emotional states contained in speech. The former feature obtained by a list of parametric filters reflects the broad frequency components and the fine structure of lower frequency components, contributed by unvoiced phones and voiced phones, respectively; the latter feature indicates the non-linearity and self-similarity of a speech signal. Comparative experiments based on Hidden Markov Model and K Nearest Neighbor methods are carried out. The results show that Cd and Fd are much more closely related with emotional expression than the features commonly used.

Microblogs are a rising social network with distinguishing features such as simplicity and convenience and has already attracted a large number of users and triggered massive information explosion concerning individuals' own statuses and opinions. While sentiment analysis of the messages in microblogs is of great value, most of present studies are on English microblogs and few are on Chinese microblogs. Compared to English, Chinese has its unique expression style, such as no spaces or other word delimiters. Furthermore, Chinese short text also has its own properties. Thus we are inspired to explore effective features for sentiment classification of Chinese short text. In this paper, we propose to study user-related sentiment classification of Chinese microblogs in terms of the statistical and semantic characteristics, and deisgn the corresponding features: ratio of positive words and negative words (PNR), position feature (POS), collocation of verbs (COL), auxiliary words (AU). Then we employ an SVM-based method to classify the sentiment. Experiments show that the features we design is effective in recognizing the sentiment of messages in microblogs.

In recent years, researchers have tried to create unhindered human-computer interaction by giving virtual agents human-like conversational skills. Predicting backchannel feedback for agent listeners has become a novel research hot-spot. The main goal of this paper is to identify appropriate features and methods for backchannel prediction in Mandarin conversations. Firstly, multimodal Mandarin conversations are recorded for the analysis of backchannel behaviors. In order to eliminate individual difference in the original face-to-face conversations, more backchannels from different listeners are gathered together. These data confirm that backchannels occurring in the speakers' pauses form a vast majority in Mandarin conversations. Both prosodic and visual features are used in backchannel prediction. Four types of models based on the speakers' pauses are built by using support vector machine classifiers. An evaluation of the pause-based prediction model has shown relatively high accuracy in consideration of the optional nature of backchannel feedback. Finally, the results of the subjective evaluation validate that the conversations performed between humans and virtual listeners using backchannels predicted by the proposed models is more unhindered compared to other backchannel prediction methods.