Affective information processing is intended to give computers the human-like capabilities of observation, interpretation and generation of affect features It is essential for natural and effective human-computer interaction and has become a very hot research topic This state-of-the-art volume discusses the latest developments in affective information processing, and summarises the key technologies researched, such as facial expression recognition, face animation, emotional speech synthesis, intelligent agents, and virtual reality The detailed and accessible coverage includes a wide range of topics, including areas which look to challenge and improve current research The book is organised into the following themed parts to aid the readers in their understanding of the field: Cognitive Emotion Model, Affect in Speech, Affect in Face and Affect in Multimodal Interaction Topics and Features: Reports on the state of the art research in affective information processing Summarises key technologies researched during the last few years, such as affective speech processing, affective facial expressions, affective body gesture and movement, affective multi-modal systems, etc Offers a foundational introductory chapter that grounds readers in core concepts and principles Discusses applications of this technology, such as in education against bullying Provides summarising conclusions throughout Describes affect in multimodal information Looks at cognitive emotion modelling in natural language communication Explores emotion perception and recognition from speech, and based on multimodal information Includes face animation based on large audio-visual database Presents physiological sensing for affective computing Examines evolutionary expression of emotions in virtual humans using lights and pixels Written to provide an opportunity for scientists, engineers and graduate students to explore the difficulties, solutions and technologies in this exciting new area, this ground-breaking book provides a comprehensive overview and significant insight into the field and will prove a valuable reference tool and resource

Affective Information Processing

Decades of scientific research on neurophysiology have proven emotions are not simply a minor aspect of human activity, but rather a fundamental one. The Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence focuses on the integration of emotions into artificial environments such as computers and robotics. Written by an international collaboration of experts within the field, this Handbook of Research covers topics such as emotion simulation and emotion synthetic development.

Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence

In this broad-based overview, we review image analysis for three classes of digital media applications: traditional signal processing related procedures, including image enhancement, restoration and compression; relatively new media creation and data retrieval problems, including computer animation and multimedia data retrieval; and pattern recognition, including document imaging and electronic books.

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Image analysis for digital media applications

Technical Section: Employing personality-rich virtual persons-New tools required

Extensive research and development have been done in the area of human simulation and artificial intelligence and their related fields, such as common sense knowledge bases, chatterbots, natural language parsing, semantic analysis, synthetic actors, and cognitive sciences. This paper takes part in that extensive research by focusing on the improvement of human simulation in chatterbots, specifically in Alicebot, a prominent non-emotional pattern-matching chatbot. An emotion and personality model is added to Alicebot so that it can make decisions based on its emotions and personality. Alicebot is also augmented with the ability to determine what it likes or does not like based on its domain concept preferences. Finally, Alicebot will be able to generate its own text without using patterns. These improvements will allow Alicebot to better simulate responses like humans.

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Emotional and Domain Concept Enhancements to Alicebot

This paper describes the generation and realistic blending of emotional facial expressions, visual speech, facial poses and other non-emotional secondary facial expressions on 3D computer graphical head models. The generation and blending of these expressions is done by means of the mathematical formulation of a psychological theory of facial expression generation. In total, 23 emotional expressions, 21 emotion blends, 19 visemes, 342 viseme blends and 37 secondary expressions and postures have been modelled, which can result in an infinite number of realistic facial expressions, due to the blending of these entities at different intensities that may vary continuously with time. The blending algorithm enables animators to script their animations at higher, more user-friendly levels or to use the results of artificial intelligence and computational psychological methods to generate and manage expressive, autonomous or near-autonomous virtual characters, without having to rely on performance-based methods.

Modelling and combining emotions, visual speech and gestures in virtual head models

Computer animation has come a long way during the last decade and is now capable of producing near-realistic rendered 3D computer graphics models of expressive, talking, acting humanoids and other characters inhabiting virtual worlds. However, the component of work that needs to be done by animators and artists in producing these synthetic character performances is quite significant. In this paper, we present an expert system based on fuzzy knowledge bases that helps in moving towards automating the task of animating virtual human heads and faces. Our Virtual Actor (Vactor) framework is based on several subsystems that use mainly fuzzy and some non-fuzzy rules to teach virtual actors to know the emotions and gestures to use in different situations. Theories of emotion, personality, dialogue, and acting, as well as empirical evidence are incorporated into our framework and knowledge bases to produce convincing results.

3D animated movie actor training using fuzzy logic

This paper describes a new and efficient method for facial expression generation on cloned synthetic head models. The system uses abstract facial muscles called action units (AUs) based on both anatomical muscles and the facial action coding system. The facial expression generation method has real-time performance, is less computationally expensive than physically based models, and has greater anatomical correspondence than rational free-form deformation or spline-based, techniques. Automatic cloning of a real human head is done by adapting a generic facial and head mesh to Cyberware laser scanned data. The conformation of the generic head to the individual data and the fitting of texture onto it are based on a fully automatic feature extraction procedure. Individual facial animation parameters are also automatically estimated during the conformation process. The entire animation system is hierarchical; emotions and visemes (the visual mouth shapes that occur during speech) are defined in terms of the AUs, and higher-level gestures are defined in terms of AUs, emotions, and visemes as well as the temporal relationships between them. The main emphasis of the paper is on the abstract muscle model, along with limited discussion on the automatic cloning process and higher-level animation control aspects.

A new efficient expression generation and automatic cloning method for multimedia actors

Computer animation has come a long way during the last decade and is now capable of producing near-realistic rendered 3D computer graphics models of expressive, talking, acting humanoids and other characters inhabiting virtual worlds. However, the component of work that needs to be done by animators and artists in producing these synthetic character performances is quite significant. In this paper, we present an expert system based on fuzzy knowledge bases that helps in moving towards automating the task of animating virtual human heads and faces. Our Virtual Actor (Vactor) framework is based on several subsystems that use mainly fuzzy and a minor degree of non-fuzzy linguistic rules to teach virtual actors to know the emotions and gestures to use in different situations. Theories of emotion, personality, dialogue, and acting, as well as empirical evidence are incorporated into our framework and knowledge bases to produce convincing results.

A fuzzy rule-based interactive methodology for training multimedia actors

Although modern computer graphics and animation are cable of producing near-realistic 3D images of virtual characters, the component of work that needs to be done by animators and artists is quite significant. A virtual actor framework developed by us aids animators in automating the modelling and animation of emotive virtual human heads with visual speech and gestures. We present the 'situation processor' component of this system that uses the OCC (Ortony, Clore and Collins) cognitive-emotional theory to intelligently query a user to determine the emotional response of a synthetic character in a movie scene. The use of our system significantly reduces the workload of animators.

Savant Karunaratne

Papers

Modelling and combining emotions, visual speech and gestures in virtual head models

3D animated movie actor training using fuzzy logic

A new efficient expression generation and automatic cloning method for multimedia actors

A fuzzy rule-based interactive methodology for training multimedia actors

Interactive emotional response computation for scriptable multimedia actors