Machine Analysis of Facial Expressions
Maja Pantić, Marian Stewart · 2007
communicated by rapid facial signals include the following (Ekman & Friesen, 1969; Pantic et al., 2006): (a) affective / attitudinal states and moods,1 e.g., joy, fear, disbelief, interest, dislike, stress, (b) emblems, i.e., culture-specific communicators like wink, (c) manipulators, i.e., self-manipulative actions like lip biting and yawns, (d) illustrators, i.e., actions accompanying speech such as eyebrow flashes, (e) regulators, i.e., conversational mediators such as the exchange of a look, head nodes and smiles. Applications of Facial Expression Measurement TechnologyGiven the significant role of the face in our emotional and social lives, it is not surprising that the potential benefits from efforts to automate the analysis of facial signals, in particular rapid facial signals, are varied and numerous (Ekman et al., 1993), especially when it comes to computer science and technologies brought to bear on these issues (Pantic, 2006).As far as natural 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 sub-systems, including automated tools for tracking gaze and focus of attention, lip reading, bimodal speech processing, face / visual speech synthesis, and face-based command issuing.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, recognizing the person's facial signals and making the avatar respond to those using synthesized speech and facial expressions is important.Combining facial expression spotting with facial expression interpretation in terms of labels like "did not understand", "disagree", "inattentive", and "approves" could be employed as a tool for monitoring human reactions during videoconferences, web-based lectures, and automated tutoring sessions.Attendees' facial expressions will inform the speaker (teacher) of the need to adjust the (instructional) presentation.The focus of the relatively recently initiated research area of affective computing lies on sensing, detecting and interpreting human affective states and devising appropriate means for handling this affective information in order to enhance current HCI designs (Picard, 1997).The tacit assumption is that in many situations human-machine interaction could be improved by the introduction of machines that can adapt to their users (think about computer-based advisors, virtual information desks, on-board computers and navigation systems, pacemakers, etc.).The information about when the existing processing should be How to referenceIn order to correctly reference this scholarly work, feel free to copy and