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Facial Expression Recognition Synonyms Definition Comp. by: SunselvakumarGalleys0000870950 Date:27/11/08 Time:14:34:41 Stage:First Proof File Path://ppdys1108/Womat3/Production/PRODENV/0000000005/0000010929/0000000016/ 0000870950.3D Proof by: QC by: F ambient intelligence, and ▶ human computing) will Facial Expression Recognition need to develop human-centered ▶ user interfaces that respond readily to naturally occurring, multimod- MAJA PANTIC al, human communication [1]. These interfaces will Imperial College London, London, UK; University of need the capacity to perceive and understand inten- Twente, AE Enschede, The Netherlands tions and emotions as communicated by social and affective signals. Motivated by this vision of the future, automated analysis of nonverbal behavior, and espe- Synonyms cially of facial behavior, has attracted increasing atten- tion in computer vision, pattern recognition, and Facial Expression Analysis; Facial Action Coding human-computer interaction [2–5]. To wit, facial ex- pression is one of the most cogent, naturally preeminent means for human beings to communicate emotions, Definition to clarify and stress what is said, to signal comprehen- sion, disagreement, and intentions, in brief, to regulate Facial expression recognition is a process performed by interactions with the environment and other persons in humans or computers, which consists of: the vicinity [6, 7]. Automatic analysis of facial expres- 1. Locating faces in the scene (e.g., in an image; this sions forms, therefore, the essence of numerous next- step is also referred to as face detection), generation-computing tools including ▶ affective 2. Extracting facial features from the detected face computing technologies (proactive and affective user region (e.g., detecting the shape of facial compo- interfaces), learner-adaptive tutoring systems, patient- nents or describing the texture of the skin in a facial profiled personal wellness technologies, etc. area; this step is referred to as facial feature extraction), 3. Analyzing the motion of facial features and/or The Process of Automatic Facial the changes in the appearance of facial features Expression Recognition and classifying this information into some facial- expression-interpretative categories such as facial The problem of machine recognition of human facial muscle activations like smile or frown, emotion (af- expression includes three subproblem areas (Fig. 1): fect) categories like happiness or anger, attitude cate- (1) finding faces in the scene, (2) extracting facial gories like (dis)liking or ambivalence, etc. (this step features from the detected face region, (3) analyzing is also referred to as facial expression interpretation). the motion of facial features and/or the changes in the appearance of facial features, and classifying this infor- mation into some facial-expression-interpretative cate- Introduction gories (e.g., emotions, facial muscle actions, etc.). The problem of finding faces can be viewed as a A widely accepted prediction is that computing segmentation problem (in machine vision) or as a will move to the background, weaving itself into the detection problem (in pattern recognition). It refers fabric of our everyday living and projecting the human to identification of all regions in the scene that contain user into the foreground. To realize this goal, next- a human face. The problem of finding faces (face local- generation computing (a.k.a. pervasive computing, ization, face detection) should be solved regardless of # Springer-Verlag Berlin Heidelberg 2009 Comp. by: SunselvakumarGalleys0000870950 Date:27/11/08 Time:14:34:44 Stage:First Proof File Path://ppdys1108/Womat3/Production/PRODENV/0000000005/0000010929/0000000016/ 0000870950.3D Proof by: QC by: 2 F Facial Expression Recognition Facial Expression Recognition. Figure 1 Outline of an automated, geometric-features-based system for facial expression recognition (for details of this system, see [4]). clutter, occlusions, and variations in head pose and Given this glossary, most of the proposed approaches lighting conditions. The presence of non-rigid move- to facial expression recognition are directed toward ments due to facial expression and a high degree of static, analytic, 2-D facial feature extraction [3, 4]. variability in facial size, color and texture make this The usually extracted facial features are either geomet- problem even more difficult. Numerous techniques ric features such as the shapes of the facial components have been developed for face detection in still images (eyes, mouth, etc.) and the locations of facial fiducial [8, 9], (see ▶ Face Localization). However, most of points (corners of the eyes, mouth, etc.), or appearance them can detect only upright faces in frontal or near- features representing the texture of the facial skin in frontal view. Arguably the most commonly employed specific facial areas including wrinkles, bulges, and face detector in automatic facial expression analysis furrows. Appearance-based features include learned is the real-time face detector proposed by Viola and image filters from Independent Component Analysis Jones [10]. (ICA), Principal Component Analysis (PCA), Local The problem of feature extraction can be viewed as Feature Analysis (LFA), Gabor filters, integral image a dimensionality reduction problem (in machine vi- filters (also known as box-filters and Haar-like filters), sion and pattern recognition). It refers to transforming features based on edge-oriented histograms, etc, (see the input data into a reduced representation set of ▶ Face Features, ▶ Skin Texture, and ▶ Feature Extrac- features which encode the relevant information from tion). Several efforts have also been reported which use the input data. The problem of facial feature extraction both geometric and appearance features (e.g., [3]). from input images may be divided into at least three These approaches to automatic facial expression anal- dimensions [2, 4]: (1) Are the features holistic (span- ysis are referred to as hybrid methods. Although it ning the whole face) or analytic (spanning subparts has been reported that methods based on geometric of the face)?; (2) Is temporal information used?; (3) features are often outperformed by those based Are the features view- or volume based (2-D/3-D)?. on appearance features using, e.g., Gabor wavelets or Comp. by: SunselvakumarGalleys0000870950 Date:27/11/08 Time:14:34:44 Stage:First Proof File Path://ppdys1108/Womat3/Production/PRODENV/0000000005/0000010929/0000000016/ 0000870950.3D Proof by: QC by: Facial Expression Recognition F 3 eigenfaces, recent studies show that in some cases well [4]. In order to address the limitations inherent geometric features can outperform the appearance- in optical flow techniques such as the accumulation of based ones [4, 11]. Yet, it seems that using both geo- error and the sensitivity to noise, occlusion, clutter, metric and appearance features might be the best and changes in illumination, recent efforts in automatic choice in the case of certain facial expressions [11]. facial expression recognition use sequential state esti- Contractions of facial muscles, which produce mation techniques (such as Kalman filter and Particle facial expressions, induce movements of the facial filter) to track facial feature points in image sequences skin and changes in the location and/or appearance of (e.g., [4, 11]). facial features (e.g., contraction of the Corrugator mus- Eventually, dense flow information, tracked move- cle induces a frown and causes the eyebrows to move ments of facial characteristic points, tracked changes towards each other, usually producing wrinkles bet- in contours of facial components, and/or extracted ween the eyebrows; Fig. 2). Such changes can be detected appearance features are translated into a description by analyzing optical flow, facial-point- or facial- of the displayed facial expression. This description component-contour-tracking results, or by using an (facial expression interpretation) is usually given either ensemble of classifiers trained to make decisions in terms of shown affective states (emotions) or in about the presence of certain changes (e.g., whether terms of activated facial muscles underlying the dis- the nasolabial furrow is deepened or not) based on played facial expression. This stems directly from two the passed appearance features. The optical flow ap- major approaches to facial expression measurement in proach to describing face motion has the advantage of psychological research [12]: message and sign judg- not requiring a facial feature extraction stage of proces- ment. The aim of message judgment is to infer what sing. Dense flow information is available throughout underlies a displayed facial expression, such as affect the entire facial area, regardless of the existence of or personality, while the aim of sign judgment is to facial components, even in the areas of smooth texture describe the ‘‘surface’’ of the shown behavior, such as such as the cheeks and the forehead. Because optical facial movement or facial component shape. Thus, a flow is the visible result of movement and is expressed brow frown can be judged as ‘‘anger’’ in a message- in terms of velocity, it can be used to represent directly judgment and as a facial movement that lowers and the facial expressions. Many researchers adopted this pulls the eyebrows closer together in a sign-judgment approach [2, 3]. Until recently, standard optical flow approach. While message judgment is all about inter- techniques were, arguably, most commonly used for pretation,
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