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facs coding manualIt breaks down facial expressions into individual components of muscle movement, called Action Units (AUs). It is often used in various scientific settings for research. It is also used by animators and computer scientists interested in facial recognition. Such skills are useful for psychotherapists, interviewers, and anyone working in communications. It also describes how AUs appear in combinations. The FACS manual was first published in 1978 by Ekman and Friesen, and was most recently revised in 2002. The Paul Ekman Group offers the manual for sale. The user reads the manual and practices coding various pictures and videos. This self-instruction usually takes about 50 to 100 hours to complete. Anyone who wants to state that they know FACS and can code in FACS must pass the FACS Final test. After completing the self-study, or a workshop, you can take the final test for certification. The Paul Ekman Group offers the FACS test for sale. If a user studies FACS five days a week for two hours a day, then learning will be closer to 50 than 100 hours. Dr. Ekman recommends training in groups. This can help make the high volume of information easier to learn. Currently there are no online or in-person versions of FACS training that have been evaluated or approved by Paul Ekman. There are also accounts in many other published academic articles. The anatomist Hjorstjo (1970) did important groundwork in identifying the units of action based on facial muscle groups on which Ekman and Friesen built in developing FACS as a measurement system. It was a manual, not an article, available for training as the current manual is. The original version is out of print, and techniques have been modified since then. The 2002 manual is the current version, and it is the only one that should be used for scoring today. Could you help us? Also, there is considerable variability in experience among FACS coders. If you are interested in hiring someone, first make sure they are FACS certified (i.e.http://www.zig.eco.pl/files/elektra-belle-epoque-manual.xml

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, have passed the FACS final test).To do this, the coder scans the video for core combinations of events that have been found to suggest certain emotions (much like the prototypes in Table 10-2). The coders only codes the events in a video record that contains such core combinations — they use FACS coding to score those events, but they are not coding everything on the video. So EMFACS is FACS selectively applied. EMFACS saves time as one is not coding everything. The drawback is that it can be harder to get intercoder agreement on EMFACS coding as the coders have to agree on two things: 1) whether to code an event (a result of their online scanning of the video for the core combinations) and 2) how to code those events that they have chosen to code. Bear in mind, EMFACS coding still yields FACS codes, so the data have to be interpreted into emotion categories. The EMFACS instructions are only available people who have passed the FACS final test. The reason is that we have to make sure people have mastery of FACS before applying rules to use FACS selectively in this way. This is based on the wishes of the authors of EMFACS — Paul Ekman and Wallace Friesen, and it makes good sense. Only people who know FACS as a comprehensive system can correctly apply it on a selective basis. If you take the final test and pass, you can receive the document. The FACS as we know it today was first published in 1978, but was substantially updated in 2002. Using FACS, we are able to determine the displayed emotion of a participant. This analysis of facial expressions is one of very few techniques available for assessing emotions in real-time ( fEMG is another option ). Other measures, such as interviews and psychometric tests, must be completed after a stimulus has been presented. This delay ultimately adds another barrier to measuring how a participant truly feels in direct response to a stimulus.http://www.karmatara.org.np/userfiles/elektra-beckum-tkhs-315-e-manual.xml Researchers have for a long time been limited to manually coding video recordings of participants according to the action units described by the FACS. This process is now possible to complete with automatic facial expression analysis. Below we have listed the major action units that are used to determine emotions. Roll your mouse over the image to start the movement. Certain combined movements of these facial muscles pertain to a displayed emotion. Emotion recognition is completed in iMotions using Affectiva, which uses the collection of certain action units to provide information about which emotion is being displayed. For example, happiness is calculated from the combination of action unit 6 (cheek raiser) and 12 (lip corner puller). A complete list of these combinations and the emotion that they relate to is shown below. The gifs on the right are shown in the same order that the action units listed. The FACS is also graded on a scale of intensity, which gives a measure of how strongly the emotion is displayed. These measurements can also be synchronized with recordings of galvanic skin response, which provides a measure of arousal. With this information combined, it’s possible to start drawing conclusions about how strongly an individual felt, and what those emotions consisted of, in response to a set stimulus. The screenshot below shows how the facial expression data is displayed while a participant watches an advertisement. If we zoom in, we can see the intensity of the displayed emotion. There are five emotions displayed in the image below, however iMotions provides a measure of the seven central emotions (shown in the table above), alongside, and in conjunction with measurements of action units. I hope this explanation of action units and FACS has been helpful, and informative. If you’d like to learn even more about facial expressions, then we also have a free pocket guide that you can download for free below! 1090 x 330 ( 1895 x 574 ) 148.http://www.statcardsports.com/node/1111255 KB 1024 x 310 ( 0 x 0 ) 49.24 KB Related Articles Product news: iMotions Mobile Research Platform Product Release News: Sensor Data preview and R Notebooks Read more about the iMotions Platform CEO: Peter Hartzbech. The FACS Manual is over 500 pages in length and provides the AUs, as well as Ekman's interpretation of their meaning.It also defines a number of Action Descriptors, which differ from AUs in that the authors of FACS have not specified the muscular basis for the action and have not distinguished specific behaviors as precisely as they have for the AUs.Computer graphical face models, such as CANDIDE or Artnatomy, allow expressions to be artificially posed by setting the desired action units.A study conducted by Vick and others (2006) suggests that FACS can be modified by taking differences in underlying morphology into account. Such considerations enable a comparison of the homologous facial movements present in humans and chimpanzees, to show that the facial expressions of both species result from extremely notable appearance changes. The development of FACS tools for different species allows the objective and anatomical study of facial expressions in communicative and emotional contexts. Examples of these are:Though muscle activation is not part of FACS, the main muscles involved in the facial expression have been added here for the benefit of the reader.The muscular basis for these actions hasn't been specified and specific behaviors haven't been distinguished as precisely as for the AUs.Palo Alto: Consulting Psychologists Press. Salt Lake City: A Human Face. Retrieved 2009-02-04. Retrieved 2011-02-23. By using this site, you agree to the Terms of Use and Privacy Policy. FACS coding, however, is labor intensive and difficult to standardize. A goal of automated FACS coding is to eliminate the need for manual coding and realize automatic recognition and analysis of facial actions. Success of this effort depends in part on access to reliably coded corpora; however, manual FACS coding remains expensive and slow. This paper proposes Fast-FACS, a computer vision aided system that improves speed and reliability of FACS coding. Three are the main novelties of the system: (1) to the best of our knowledge, this is the first paper to predict onsets and offsets from peaks, (2) use Active Appearance Models for computer assisted FACS coding, (3) learn an optimal metric to predict onsets and offsets from peaks. The system was tested in the RU-FACS database, which consists of natural facial behavior during a two-person interview. Fast-FACS reduced manual coding time by nearly 50 and demonstrated strong concurrent validity with manual FACS coding. Keywords Facial Action Coding System Action Unit Recognition This is a preview of subscription content, log in to check access. Preview Unable to display preview. Download preview PDF. Unable to display preview. References 1. Ekman, P., Friesen, W.: Facial Action Coding System: A technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto (1978) Google Scholar 2. Cohn, J.F., Kanade, T.: Use of automated facial image analysis for measurement of emotion expression. In: The Handbook of Emotion Elicitation and Assessment. Series in Affective Science. Oxford University Press, Oxford (2007) Google Scholar 3. Bartlett, M., Littlewort, G., Lainscsek, C., Fasel, I., Frank, M., Movellan, J.: Fully automatic facial action recognition in spontaneous behavior. In: 7th International Conference on Automatic Face and Gesture Recognition (2006) Google Scholar 4. Pantic, M., Patras, I.: Dynamics of Facial Expression: Recognition of Facial Actions and their Temporal Segments from Face Profile Image Sequences. In: Harrigan, J.A., Rosenthal, R., Scherer, K. (eds.) Handbook of Nonverbal Behavior Research Methods in the Affective Sciences, NY, Oxford (2005) Google Scholar 6. Valstar, M., Pantic, M.: Combined support vector machines and hidden Markov models for modeling facial action temporal dynamics. In: Proceedings of IEEE Workshop on Human Computer Interaction. In: International Conference on Computer Vision (2007) Google Scholar 9. De la Torre, F., Nguyen, M.: Parameterized kernel principal component analysis: Theory and applications to supervised and unsupervised image alignment. In: IEEE Computer Vision and Pattern Recognition (2008) Google Scholar 10. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. Matthews, I., Baker, S.: Active appearance models revisited.Noldus information technology. Observer XT 10, The Netherlands (2011) Google Scholar 16. Sayette, M.A., Cohn, J.F., Wertz, J.M., Perrott, M., Parrott, D.J.: A psychometric evaluation of the facial action coding system for assessing spontaneous expression. In: Coan, J.A., Allen, J.J.B. (eds.) The Handbook of Emotion Elicitation and Assessment. Oxford University Press, Oxford (2007) Google Scholar 18. Ekman, P., Friesen, W., Hager, J.: Facial action coding system: Research nexus. Network Research Information, Salt Lake City, UT (2002) Google Scholar 19. Fleiss, J.: Statistical methods for rates and proportions.In: D’Mello S., Graesser A., Schuller B., Martin JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6974. Springer, Berlin, Heidelberg. This page doesn't support Internet Explorer 6, 7 and 8. Please upgrade your browser or activate Google Chrome Frame to improve your experience. There is broad interest in the application of FACS for assessing consumer expressions as an indication of emotions to consumer product-stimuli. However, the translation of FACS to characterization of emotions is elusive in the literature. The aim of this systematic review is to give an overview of how FACS has been used to investigate human emotional behavior to consumer product-based stimuli. The search was limited to studies published in English after 1978, conducted on humans, using FACS or its action units to investigate affect, where emotional response is elicited by consumer product-based stimuli evoking at least one of the five senses. The search resulted in an initial total of 1,935 records, of which 55 studies were extracted and categorized based on the outcomes of interest including (i) method of FACS implementation; (ii) purpose of study; (iii) consumer product-based stimuli used; and (iv) measures of affect validation. The vast majority of studies (53) did not validate FACS-determined affect and, of the validation measures that were used, most tended to be discontinuous in nature and only captured affect as it holistically related to an experience. This review illuminated some inconsistencies in how FACS is carried out as well as how emotional response is inferred from facial muscle activation. This may prompt researchers to consider measuring the total consumer experience by employing a variety of methodologies in addition to FACS and its emotion-based interpretation guide.Emotions are short-term, complex, multidimensional behavioral responses ( Smith and Ellsworth, 1985; Lambie and Marcel, 2002 ) that are aimed to promote adaptive strategies in a variety of contexts.Positive affect refers to the extent an individual feels enthusiastic, active, and pleasurably aroused whereas negative affect refers to the extent an individual feels upset, distressed, and unpleasantly aroused ( Watson and Tellegen, 1985 ). However, Lazarus ( Novacek and Lazarus, 1990; Lazarus, 1991a, b ) proposed a theory in which motivational processes played a central role in emotional expression. In this theory, emotions such as pride, love, or happiness would arise when a situation is regarded as beneficial and anger, anxiety, and sadness would arise when a situation is regarded as harmful. Davidson (1984) proposed a similar model linked to frontal electroencephalogram (EEG) asymmetry in the brain during emotional states. He proposed that frontal asymmetry was not related to the valence of emotional stimulus but rather to the motivational system that is engaged by the stimulus. He concluded that emotion will be associated with a right or left asymmetry depending on the extent to which it is accompanied by approach or withdrawal behaviors ( Davidson, 1984 ). Recent work has supported distinguishing facially expressed emotions as approach or withdrawal based on the relationship between emotions and cognitive processes ( Coan et al., 2001; Alves et al., 2009; van Peer et al., 2010 ). From this research, Ekman et al. (1972) hypothesized that the face may also influence other people's emotional experiences by providing signals about how an individual feels. In an effort to provide a sounder basis about what different facial actions might signify, Ekman and Friesen (1976, 1978) developed a novel technique for measuring facial behavior, the Facial Action Coding System (FACS). FACS was primarily developed as a comprehensive system to distinguish all possible visible anatomically based facial movements ( Ekman and Friesen, 1976 ). FACS provides a common nomenclature for facial movement research, which allows for diverse application in a variety of fields. Ekman et al. (2002) later published a significant update to FACS. A constraint of FACS is that it deals with clearly visible changes in facial movement and doesn't account for subtle visible changes such as changes in muscle tonus ( Ekman and Friesen, 1976 ). Another limitation of FACS is that it was developed to measure movement in the face, thus other facial phenomena (e.g., changes in skin coloration, sweating, tears, etc.) are excluded. AFEA provides more rapid evaluation of facial expressions and subsequent classification of those expressions into discrete categories of basic emotions (happy, sad, disgusted, surprised, angry, scared, and neutral) on a scale from 0 (not expressed) to 1 (fully expressed) ( Lewinski et al., 2014d ). Additionally, software may assess AU activation, intensity, and duration. Several commercially available software systems can generate AFEA. The face reading software functions by finding a person's face and subsequently creating a 3D Active Appearance Model (AAM) ( Cootes and Taylor, 2000 ) of the face. The AAM serves as a base for which all the universal emotional expressions, plus the neutral expression, can be identified via independent neural network classifiers trained with backpropagation—a method proven effective for pattern recognition tasks ( Bishop, 1995 ). The final expression judgement of a face image is then based on the network with the highest output.Additional approaches exist, such as those from the field of neuromarketing, to understand how internal and external forces (e.g., an individual's internal emotional experience vs.Likewise, facial electromyography (EMG) serves to measure and map the underlying electrical activity that is generated when muscles contract during discrete choice-making ( Rasch et al., 2015 ) while eye-tracking technology has been harnessed to study gaze behavior with respect to packaging, label and menu design, in-store consumer behavior, emotional responses, and eating disorders ( Duerrschmid and Danner, 2018 ). In fact, neuromarketing techniques are often used in conjunction with FACS to provide a more holistic picture of the consumer affective experience ( Cohn et al., 2002; Balzarotti et al., 2014; Lagast et al., 2017 ). This review will outline the methods and purposes for using FACS to characterize human affective (i.e., emotional) responses elicited by consumer product-based stimuli and, additionally, will provide evidence for how FACS-measured affective responses are validated. The syntax was developed in line with common search strategies in consumer and sensory research ( Booth, 2014 ) and in line with studies on emotion in the field of psychology ( Mauss and Robinson, 2009 ). The search included an a priori limit for only human studies and restricted the publication year to studies published after 1978 since the defining work on the Facial Action Coding System was published in 1978 ( Ekman and Friesen, 1978 ). To be included in the systematic review, studies had to be published in English, used the FACS system or its AUs to evaluate facial response to purchasable consumer-based goods or products (stimuli) that evoke at least one of the five senses (sight, touch, smell, taste, hearing), and reported outcomes on emotion, mood, or arousal and outcomes on measures used to validate emotions, mood, or arousal. Inclusion and exclusion criteria are shown in Table S1. This review focused on research studies that used FACS to measure human affective responses to consumer product-based stimuli with no limitation in setting. As this review aims to compare different methods of FACS implementation, no exclusions were made based on comparison. Additionally, known systematic reviews on the facial action coding system or facial expression of emotion were considered and both their cited and citing sources were reviewed for potential inclusion in this study. Included studies' reference lists and studies citing the included studies were also reviewed for inclusion. Two researchers conducted the search independently, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement ( Moher et al., 2009 ), using the same databases and all findings were merged. If the researchers encountered conflicts in their independent assessments, they were discussed until a consensus was reached. The first step of the search was based on a title and abstract screening for existence of important key words related to the research question and for relevance of the studies based on the inclusion and exclusion criteria. In the second step, all relevant articles were subjected to an in-depth full-text critical review for eligibility. Additionally, 9 studies were later included after reviewing reference lists and studies citing the included studies. A total of 638 duplicates were removed, resulting in 1,298 records to be screened. Based on title and abstract screening for existence of important keywords related to research question as well as inclusion and exclusion criteria, 1,026 were found to be irrelevant and 271 records were subject to a full-text review. A total 55 articles were selected for extraction and analysis. The search strategy for this systematic review can be found in Figure 1, which is based on the PRISMA ( Moher et al., 2009 ). Identifying information extracted from the studies including the title, author(s), publishing year, and Covidence number were placed into separate columns. Columns were also made for information extracted for each outcome of interest (i) method of FACS implementation (ii) purpose of study (iii) consumer product-based stimuli used and (iv) measures of affect validation; a “Notes” column was also made so further detail could be provided on each outcome of interest, if necessary ( Table S3 ). Method of FACS implementation consisted of three broad categories: manual, automatic, and “both” (a combination of manual as well as automatic). For purpose of study, typology-based categories were developed to structure the plethora of aims represented across the extracted studies ( Table S4 ). Likewise, typology-based categories were developed to structure the diversity of diversity of purchasable, consumer product-based stimuli that were represented across the extracted studies ( Table S4 ). If a study utilized multiple stimuli, then the “Multiple” category was chosen, and all stimuli were listed out in the “Notes” column of Table S3. An variety of affect validation measures represented across the extracted studies ( Table S4 ). The category “None” was used to describe studies where no additional measure was used to validate FACS-measured affective response. For interpretations for key words and terms used throughout the review has been provided ( Table S5 ). If the researchers encountered conflicts in their independent assessments, they were discussed until a consensus was reached. Bias was assessed in the following categories: selection bias (allocation concealment, sequence generation), performance bias (blinding of participants and personnel to all outcomes), detection bias (blinding of assessors to all outcomes), attrition bias (incomplete outcome data), reporting bias (selective outcome reporting), and other sources of bias. The two researchers conducted the risk of bias assessment independently and evaluated the extracted studies for selection, performance, detection, attrition, reporting, and other sources of bias, which was rated as being either low, high, or unclear. If the researchers encountered conflicts in their independent assessments, they were discussed until a consensus was reached. More than half of these articles were published in the last 10 years (33 studies; 60) of which a majority have been published in the last 5 years (19 studies; 35). About 13 times more articles were published in the last 4 years than during the first 4 years ( Figure 2 ). This suggests that there is a growing interest in using FACS to measure human emotional behavior to consumer product-based stimuli. Figure 3 gives an overview of the consumer product-based stimuli that have been used to elicit an affective response. Most often, studies validated an individual's FACS-determined affective response with non-vocalized self-reported measures (12 studies; 22) via questionnaires, scales, or surveys. However, it should be noted that the vast majority of studies (29 studies; 53) did not use another measure to validate an individual's affective response as determined using FACS. Only 11 studies (20) exclusively coded FACS automatically, of which 4 studies utilized the Computer Expression Recognition Toolbox (CERT), 6 studies used a novel software developed by the researchers, and 1 study utilized Affdex for Market Research by Affectiva. A total of 4 studies combined manually coding FACS with another measure that automatically coded FACS to determine participants' affective responses.However, some studies used one (5 studies), three (6 studies), four (1 study), and even six people (1 study) to manually code FACS. In the study by Catia et al. (2017), it was unclear how many individuals were utilized to code the human participants' facial responses. Notably, the majority of these studies (22 out of 40; 55) did not use another measure to validate FACS-determined affective response even though it has been suggested that there are many facets to evoking emotion and any single measure would fail to capture these facets in their totality ( Lagast et al., 2017; Kaneko et al., 2018 ). However, in three studies (27), the consumer product-based stimuli themselves (e.g., robot or animatronic toy doll) contained the automatic FACS coding software. Similar to the manually coded studies, the majority of the automatically coded studies (8 out of 11; 73) did not use another measure to validate FACS-determined affective response. Though the methods varied, they encompassed all of the validation categories presented in this review (except “None”). Nevertheless, some of the reviewed studies investigated the underpinnings of emotion and their relationship to human physiological capacities. In two studies, Haase et al. (2015) examined the effect of the short allele of the 5-HTTLPR polymorphism in the serotonin transporter gene on positive emotional expressions measured by objectively coded smiling and laughing behavior in response to cartoons as well as an amusing film. Cohn et al. (2002) assessed individual differences in rates of positive expression and in specific configurations of facial action units while participants watched short films intended to elicit emotion, which showed strong evidence that individuals may be accurately recognized on the basis of their facial behavior suggesting that facial expression may be a valuable biometric. Participants in the study by Weiland et al. (2010) were exposed to basic tastes (bitter, salty, sweet, sour, umami) as well as qualitatively different odors (banana, cinnamon, clove, coffee, fish, and garlic) while their taste- and odor- specific facial expressions were examined. Spontaneous facial expressions were also examined in response to distaste ( Chapman et al., 2017 ) and to investigate whether they would provide additional information as to the explicit measure of liking ( de Wijk et al., 2012 ) for basic tastes. For a more detailed overview of the studies categorized by purpose and consumer product-based stimuli, see Table S3. Given the aforementioned variety of purposes for affective response measurement, it appears that FACS can be applied within numerous scientific fields. A more detailed description of the stimuli used in each study can be found in the “Notes” column of Table S3. Although it could be argued that flavor or odor themselves are not consumer product-based stimuli, the reviewers deemed them appropriate categories since flavor and odor are inherent properties of purchasable consumer-product based stimuli (e.g., diffusible scents such those found in essential oils, candles, room or fabric fresheners, line-extension flavor varieties, etc.) that can elicit an affective response by evoking at least one of the five senses (i.e., taste or smell), which is in accord with the inclusion criteria of this review.Grafsgaard et al. (2011), Grafsgaard et al. (2013), Grafsgaard et al. (2014) used either manual or automatic FACS coding to determine students' affective responses, while being tutored through an online program, because affective states often influence progress on learning tasks, resulting in positive or negative cycles of affect that impact learning outcomes ( Woolf et al., 2009; Baker et al., 2010; D'Mello and Graesser, 2014 ). Participants of studies conducted by Sayette and Hufford (1995), Sayette and Parrott (1999), Sayette et al. (2001), Sayette et al. (2019), Griffin and Sayette (2008) and Sayers and Sayette (2013) underwent smoking cue exposure with cigarette and rolled-paper stimuli to better understand the relationship between affect and human behaviors including craving and urge. Children's facial expressions of affect were also manually coded in response to physical games to better understand human psychological development ( Unzner and Schneider, 1990; Schneider and Josephs, 1991 ). Although food, used in studies by Forestell and Mennella (2012) as well as Gurbuz and Toga (2018), is recognized as emotional stimuli, researchers debate whether the nature of specific foods have the capacity to elicit intense emotional responses ( Desmet and Schifferstein, 2008; Walsh et al., 2017a, b ).