Face for Interface

Face for Interface

Maja Pantic (Imperial College London, UK)
DOI: 10.4018/978-1-60566-014-1.ch075
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Abstract

The human face is involved in an impressive variety of different activities. It houses the majority of our sensory apparatus: eyes, ears, mouth, and nose, allowing the bearer to see, hear, taste, and smell. Apart from these biological functions, the human face provides a number of signals essential for interpersonal communication in our social life. The face houses the speech production apparatus and is used to identify other members of the species, to regulate the conversation by gazing or nodding, and to interpret what has been said by lip reading. It is our direct and naturally preeminent means of communicating and understanding somebody’s affective state and intentions on the basis of the shown facial expression (Lewis & Haviland-Jones, 2000). Personality, attractiveness, age, and gender can also be seen from someone’s face. Thus the face is a multisignal sender/receiver capable of tremendous flexibility and specificity. In general, the face conveys information via four kinds of signals listed in Table 1. Automating the analysis of facial signals, especially rapid facial signals, would be highly beneficial for fields as diverse as security, behavioral science, medicine, communication, and education. In security contexts, facial expressions play a crucial role in establishing or detracting from credibility. In medicine, facial expressions are the direct means to identify when specific mental processes are occurring. In education, pupils’ facial expressions inform the teacher of the need to adjust the instructional message. As far as natural user interfaces between humans and computers (PCs/robots/machines) are concerned, facial expressions provide a way to communicate basic information about needs and demands to the machine. In fact, automatic analysis of rapid facial signals seem to have a natural place in various vision subsystems and vision-based interfaces (face-for-interface tools), including automated tools for gaze and focus of attention tracking, lip reading, bimodal speech processing, face/visual speech synthesis, face-based command issuing, and facial affect processing. Where the user is looking (i.e., gaze tracking) can be effectively used to free computer users from the classic keyboard and mouse. Also, certain facial signals (e.g., a wink) can be associated with certain commands (e.g., a mouse click) offering an alternative to traditional keyboard and mouse commands. The human capability to “hear” in noisy environments by means of lip reading is the basis for bimodal (audiovisual) speech processing that can lead to the realization of robust speech-driven interfaces. To make a believable “talking head” (avatar) representing a real person, tracking the person’s facial signals and making the avatar mimic those using synthesized speech and facial expressions is compulsory. The human ability to read emotions from someone’s facial expressions is the basis of facial affect processing that can lead to expanding user interfaces with emotional communication and, in turn, to obtaining a more flexible, adaptable, and natural affective interfaces between humans and machines. More specifically, the information about when the existing interaction/processing should be adapted, the importance of such an adaptation, and how the interaction/ reasoning should be adapted, involves information about how the user feels (e.g., confused, irritated, tired, interested). Examples of affect-sensitive user interfaces are still rare, unfortunately, and include the systems of Lisetti and Nasoz (2002), Maat and Pantic (2006), and Kapoor, Burleson, and Picard (2007). It is this wide range of principle driving applications that has lent a special impetus to the research problem of automatic facial expression analysis and produced a surge of interest in this research topic.
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Introduction: The Human Face

The human face is involved in an impressive variety of different activities. It houses the majority of our sensory apparatus: eyes, ears, mouth, and nose, allowing the bearer to see, hear, taste, and smell. Apart from these biological functions, the human face provides a number of signals essential for interpersonal communication in our social life. The face houses the speech production apparatus and is used to identify other members of the species, to regulate the conversation by gazing or nodding, and to interpret what has been said by lip reading. It is our direct and naturally preeminent means of communicating and understanding somebody’s affective state and intentions on the basis of the shown facial expression (Lewis & Haviland-Jones, 2000). Personality, attractiveness, age, and gender can also be seen from someone’s face. Thus the face is a multisignal sender/receiver capable of tremendous flexibility and specificity. In general, the face conveys information via four kinds of signals listed in Table 1.

Table 1.
Four types of facial signals
Static facial signals represent relatively permanent features of the face, such as the bony structure, the soft tissue, and the overall proportions of the face. These signals are usually exploited for person identification.
Slow facial signals represent changes in the appearance of the face that occur gradually over time, such as development of permanent wrinkles and changes in skin texture. These signals can be used for assessing the age of an individual.
Artificial signals are exogenous features of the face such as glasses and cosmetics. These signals provide additional information that can be used for gender recognition.
• • Rapid facial signals represent temporal changes in neuromuscular activity that may lead to visually detectable changes in facial appearance, including blushing and tears. These (atomic facial) signals underlie facial expressions.

Automating the analysis of facial signals, especially rapid facial signals, would be highly beneficial for fields as diverse as security, behavioral science, medicine, communication, and education. In security contexts, facial expressions play a crucial role in establishing or detracting from credibility. In medicine, facial expressions are the direct means to identify when specific mental processes are occurring. In education, pupils’ facial expressions inform the teacher of the need to adjust the instructional message.

Key Terms in this Chapter

Lip Reading: The human ability to “hear” in noisy environments by analyzing visible speech signals, that is, by analyzing the movements of the lips and the surrounding facial region. Integrating both visual speech processing and acoustic speech processing results in a more robust bimodal (audiovisual) speech processing.

Automatic Facial Expression Analysis: A process of locating the face in an input image, extracting facial features from the detected face region, and classifying these data into some facial-expression-interpretative categories such as facial muscle action categories, emotion (affect) categories, attitude categories, and so forth.

Face Synthesis: A process of creating a “talking head” which is able to speak, to display (appropriate) lip movements during speech, and to display expressive facial movements.

Machine Vision: A field of computer science concerned with the question of how to construct computer programs that automatically analyze images and produce descriptions of what is imaged.

Face-Based Interface: Regulating (at least partially) the command flow that streams between the user and the computer by means of facial signals. This means associating certain commands (e.g., mouse pointing, mose clicking, etc.) with certain facial signals (e.g., gaze direction, winking, etc.). Face-based interface can be effectively used to free computer users from classic keyboard and mouse commands.

Ambient Intelligence: The merging of mobile communications and sensing technologies, with the aim of enabling a pervasive and unobtrusive intelligence in the surrounding environment supporting the activities and interactions of the users. Technologies like face-based interfaces and affective computing are inherent ambient-intelligence technologies.

Machine Learning: A field of computer science concerned with the question of how to construct computer programs that automatically improve with experience. The key algorithms that form the core of machine learning include neural networks, genetic algorithms, support vector machines, Bayesian networks, and Markov models.

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