Behavior Research Methods The final publication is available at Springer via Spontaneous facial expression in unscripted social interactions can be measured automatically Jeffrey M. Girard University of Pittsburgh University of Pittsburgh Carnegie Mellon University Michael A. Sayette Carnegie Mellon University University of Pittsburgh Fernando De la Torre Carnegie Mellon University Methods to assess individual facial actions have potential to shed light on important behavioralphenomena ranging from emotion and social interaction to psychological disorders and health.
However, manual coding of such actions is labor intensive and requires extensive training. Todate, establishing reliable automated coding of unscripted facial actions has been a dauntingchallenge impeding development of psychological theories and applications requiring facialexpression assessment. It is therefore essential that automated coding systems be developedwith enough precision and robustness to ease the burden of manual coding in challenging datainvolving variation in participant gender, ethnicity, head pose, speech, and occlusion. We reporta major advance in automated coding of spontaneous facial actions during an unscripted socialinteraction involving three strangers. For each participant (n = 80, 47% women, 15% Non-white), 25 facial action units (AUs) were manually coded from video using the Facial ActionCoding System. Twelve AUs occurred more than 3% of the time and were processed usingautomated FACS coding. Automated coding showed very strong reliability for the proportionof time that each AU occurred (mean intraclass correlation = 0.89), and the more stringentcriterion of frame-by-frame reliability was moderate to strong (mean Matthew's correlation =0.61). With few exceptions, differences in AU detection related to gender, ethnicity, pose, andaverage pixel intensity were small. Fewer than 6% of frames could be coded manually but notautomatically. These findings suggest automated FACS coding has progressed sufficiently tobe applied to observational research in emotion and related areas of study.
Keywords: facial expression, FACS, affective computing, automated coding Jeffrey M. Girard, Department of Psychology, University of During the past few decades, some of the most strik- Pittsburgh; Jeffrey F. Cohn, Department of Psychology, University ing findings about affective disorders, schizophrenia, addic- of Pittsburgh, The Robotics Institute, Carnegie Mellon University; tion, developmental psychopathology, and health have been Laszlo A. Jeni, The Robotics Institute, Carnegie Mellon Univer- based on sophisticated coding of facial expressions.
sity; Michael A. Sayette, Department of Psychology, University of instance, it has been found that facial expression coding Pittsburgh; Fernando De la Torre, The Robotics Institute, Carnegie using the Facial Action Coding System (FACS), which is Mellon University.
the most comprehensive system for coding facial behav- This work was supported in part by US National Institutes of ior (Ekman, Friesen, & Hager, 2002), identifies which de- Health grants R01 MH051435 and R01 AA015773.
pressed patients are at greatest risk for reattempting sui- Correspondence concerning this article should be addressed to cide (Archinard, Haynal-Reymond, & Heller, 2000); consti- Jeffrey Girard, 4325 Sennott Square, University of Pittsburgh, Pitts- tutes an index of physical pain with desirable psychometric burgh, PA 15260. Email: [email protected] properties (Prkachin & Solomon, 2008); distinguishes dif-ferent types of adolescent behavior problems (Keltner, Mof-fitt, & Stouthamer-Loeber, 1995); and distinguishes betweenEuropean-American, Japanese, and Chinese infants (Camras JEFFREY M. GIRARD et al., 1998). These findings have offered glimpses into criti- more challenging task of AU detection during spontaneous cal areas of human behavior that were not possible using ex- facial behavior. Examples of the latter include AU detec- isting methods of assessment, often generating considerable tion in physical pain (G. C. Littlewort, Bartlett, & Lee, 2009; research excitement and media attention.
P. Lucey, Cohn, Howlett, Member, & Sridharan, 2011), inter- As striking as these original findings were, it is just as views (Bartlett et al., 2006; Girard, Cohn, Mahoor, Mavadati, striking how little follow-up work has occurred using these Hammal, & Rosenwald, 2013; S. Lucey, Matthews, Am- methods. The two primary reasons for this curious state of badar, De la Torre, & Cohn, 2006), and computer-mediated affairs are the intensive training required to learn facial ex- tasks such as watching a video clip or filling out a form pression coding and the extremely time-consuming nature of (Hoque, McDuff, & Picard, 2012; Grafsgaard, Wiggins, the coding itself. Paul Ekman, one of the creators of FACS, Boyer, Wiebe, & Lester, 2013; G. Littlewort et al., 2011; notes that certification in FACS requires about 6 months of Mavadati, Mahoor, Bartlett, Trinh, & Cohn, 2013; McDuff, training and that FACS coding a single minute of video can El Kaliouby, Kodra, & Picard, 2013).
take over an hour (Ekman, 1982).
While much progress has been made, the current state of FACS (Ekman & Friesen, 1978; Ekman et al., 2002) is an the science is limited in several key respects. Stimuli to elicit anatomically-based system for measuring nearly all visually- spontaneous facial actions have been highly controlled (e.g., discernible facial movement. FACS describes facial activ- watching pre-selected video clips or replying to structured ities in terms of unique facial action units (AUs), which interviews) and camera orientation has been frontal with lit- correspond to the contraction of one or more facial mus- tle or no variation in head pose. Non-frontal pose matters cles. Any facial expression may be represented as a single because the face looks different when viewed from different AU or a combination of multiple AUs. For example, the orientations and parts of the face may become self-occluded.
Duchenne smile (i.e., enjoyment smile) is indicated by si- Rapid head movement also may be difficult to automatically multaneous contraction of the zygomatic major (AU 12) and track through a video sequence. Head motion and orienta- orbicularis oculi pars lateralis (AU 6). Although there are tion to the camera are important if AU detection is to be alternative systems for characterizing facial expression (e.g., accomplished in social settings where facial expressions of- Izard, 1979; Abrantes & Pereira, 1999), FACS is recognized ten co-occur with head motion. For example, the face and as the most comprehensive and objective means for measur- head pitch forward and laterally during social embarrassment ing facial movement currently available, and it has become (Keltner et al., 1995; Ambadar, Cohn, & Reed, 2009). Kraut the standard for facial measurement in behavioral research and Johnston (1979) found that successful bowlers smile (Cohn & Ekman, 2005; Ekman & Rosenberg, 2005).
only as they turn away from the bowling lane and toward Given the often-prohibitive time commitment of FACS their friends.
coding, there has been great interest in developing computer Whether automated methods can detect spontaneous fa- vision methods for automating facial expression coding. If cial expressions in the presence of head pose variation is un- successful, these methods would greatly improve the effi- known, as too few studies have encountered or reported on ciency and reliability of AU detection, and importantly make it. Messinger, Mahoor, Chow, and Cohn (2009) encountered its use feasible in applied settings outside of research.
out-of-plane head motion in video of infants, but neglected Although the advantages of automated facial expression to report whether it affected AU detection. Cohn and Sayette coding are apparent, the challenges of developing such sys- (2010) reported preliminary evidence that AU detection may tems are considerable. While human observers easily ac- be robust to pose variation up to 15 degrees from frontal.
commodate variations in pose, scale, illumination, occlusion, Similarly, we know little about the effects of gender and eth- and individual differences (e.g., gender and ethnicity), these nicity on AU detection. Face shape and texture vary between and other sources of variation represent considerable chal- men and women (Bruce & Young, 1998), and may be further lenges for a computer vision system. Further, there is the ma- altered through the use of cosmetics. Skin color is an addi- chine learning challenge of automatically detecting actions tional factor that may affect AU detection. Accordingly, little that require significant training and expertise even for human is known about the operational parameters of automated AU detection. For these reasons, automated FACS coding must There has been significant effort to develop computer- prove robust to these challenges.
vision based approaches to automated facial expression anal- The current study evaluates automated FACS coding us- ysis. Most of this work has focused on prototypic emotion ing a database that is well-suited to testing just how far au- expressions (e.g., joy and anger) in posed behavior. Zeng, tomated methods have progressed, and how close we are to Pantic, Roisman, and Huang (2009) have reviewed this lit- using them to study naturally-occurring facial expressions.
erature through 2009. Within the past few years, studies This investigation focuses on spontaneous facial expression have progressed to AU detection in actor portrayals of emo- in a far larger database (over 400,000 video frames from 80 tion (Valstar, Bihan, Mehu, Pantic, & Scherer, 2011) and the people) than ever attempted; it includes men and women, FACIAL EXPRESSION CAN BE MEASURED AUTOMATICALLY Whites and Nonwhites, and a wide range of facial AUs that vary in intensity and head orientation. Because this database contains variation in head pose and participant gender, as well as moderate variation in illumination and participant ethnicity, we can examine their effect on AU detection. To demonstrate automated AU detection in such a challenging database would mark a crucial step toward the goal of es- tablishing fully-automated systems capable of use in varied research and applied settings.
Figure 1. Base rates of all the coded facial action units from a subset of the data (n = 56) The current study used digital video from 80 participants (53% male, 85% white, average age 22.2 years) who were The laboratory included a custom-designed video control participating in a larger study on the impact of alcohol on system that permitted synchronized video output for each group formation processes (for elaboration, see Sayette et participant, as well as an overhead shot of the group. The al., 2012). They were randomly assigned to groups of 3 un- individual view for each participant was used in this report.
acquainted participants. Whenever possible, all three partic- The video data collected by each camera had a standard ipants in a group were analyzed. Some participants were not frame rate of 29.97 frames per second and a resolution of analyzable due to excessive occlusion from hair or head wear 640×480 pixels. Audio was recorded from a single micro- (n = 6) or gum chewing (n = 1). Participants were randomly phone. The automated FACS coding system was processed assigned to drink isovolumic alcoholic beverages (n = 31), on a Dell T5600 workstation with 128GB of RAM and dual placebo beverages (n = 21), or nonalcoholic control bever- Xeon E5 processors. The system also runs on standard desk- ages (n = 28); all participants in a group drank the same top computers.
type of beverage. The majority of participants were fromgroups with a mixed gender composition of two males and Manual FACS Coding one female (n = 32) or two females and one male (n = 26),although some were from all male (n = 12) or all female The FACS manual (Ekman et al., 2002) defines 32 distinct (n = 10) groups. All participants reported that they had not facial action units. All but 7 were manually coded. Omitted consumed alcohol or psychoactive drugs (except nicotine or were three "optional" AUs related to eye closure (AUs 43, 45, caffeine) during the 24 hour period leading up to the obser- and 46), three AUs related to mouth opening or closure (AUs 8, 25, and 26), and one AU that occurs on the neck ratherthan the face (AU 21). The remaining 25 AUs were manually Setting and Equipment coded from onset (start) to offset (stop) by one of two certi-fied and highly experienced FACS coders using Observer XT All participants were previously unacquainted. They first software (Noldus Information Technology, 2013). AU onsets met only after entering the observation room where they were were annotated when they reached slight or B level intensity seated approximately equidistantly from each other around a according to FACS; the corresponding offsets were annotated circular (75 cm diameter) table. They were asked to con- when they fell below B level intensity. AU of lower intensity sume a beverage consisting of cranberry juice or cranberry (i.e., A level intensity) are ambiguous and difficult to detect juice and vodka (a 0.82 g/kg dose of alcohol for males and a for both manual and automated coders. The original FACS 0.74 g/kg dose of alcohol for females) before engaging in a manual (Ekman & Friesen, 1978) did not code A level inten- variety of cognitive tasks. We focus on a portion of the 36- sity (referred to there as "trace."). All AUs were annotated minute unstructured observation period in which participants during speech.
became acquainted with each other (mean duration 2.69 min- Because highly skewed class distributions severely atten- utes). Separate wall-mounted cameras faced each person.
uate measures of classifier performance (Jeni, Cohn, & De It was initially explained that the cameras were focused on la Torre, 2013), AUs that occurred less than about 3% of the their drinks and would be used to monitor their consump- time were excluded from analysis. Thirteen AUs were omit- tion rate from the adjoining room, although participants later ted on this account. Five of them either never occurred or were told of our interest in observing their behavior and a occurred less than 1% of the time. Manual coding of these second consent form was signed if participants were willing.
five AUs was suspended after the first 56 subjects. Visual All participants consented to this use of their data.
inspection of Figure 1 reveals that there was a large gap be- JEFFREY M. GIRARD The first step in automatically detecting AUs was to locate the face and facial landmarks.
Landmarks refer to points that define the shape of perma- nent facial features, such as the eyes and lips. This step Image Metrics Tracking was accomplished using the LiveDriver SDK (Image Met- rics, 2013), which is a generic tracker that requires no indi-vidualized training to track facial landmarks of persons it has never seen before. It locates the two-dimensional coordinatesof 64 facial landmarks in each image. These landmarks cor- respond to important facial points such as the eye and mouthcorners, the tip of the nose, and the eyebrows. LiveDriver SVM Classification SDK also tracks head pose in three dimensions for each video frame: pitch (i.e., vertical motion such as nodding), yaw (i.e., Similarity Normalization horizontal motion such as shaking the head), and roll (i.e., lateral motion such as tipping the head sideways).
Shape and texture information can only be used to iden- tify facial expressions if the confounding influence of headmotion is controlled (De la Torre & Cohn, 2011). Because participants exhibited a great deal of rigid head motion dur-ing the group formation task, the second step was to remove the influence of such motion on each image. Many tech-niques for alignment and registration are possible (Zeng et Figure 2. Automated FACS Coding Pipeline.
al., 2009); we chose the widely-used similarity transforma-tion (Szeliski, 2011) to warp the facial images to the averagepose and a size of 128×128 pixels, thereby creating a com- tween the AUs that occurred approximately 10% or more of mon space in which to compare them. In this way, variation the time and those that occurred approximately 3% or less of in head size and orientation would not confound the mea- the time. The class distributions of the excluded AUs were surement of facial actions.
at least 3 times more skewed than those of the included AUs.
Feature Extraction.
Once the facial landmarks had In all, 12 AUs met base-rate criteria and were included for been located and normalized, the third step was to measure automatic FACS coding.
the deformation of the face caused by expression. This was To assess inter-observer reliability, video from 17 partici- accomplished by extracting Scale-Invariant Feature Trans- pants was annotated by both coders. Mean frame-level reli- form (SIFT) descriptors (Lowe, 1999) in localized regions ability was quantified with the Matthews Correlation Coeffi- surrounding each facial landmark. SIFT applies a geomet- cient (MCC), which is robust to agreement due to chance as ric descriptor to an image region and measures features that described below. The average MCC was 0.80, ranging from correspond to changes in facial texture and orientation (e.g., 0.69 for AU 24 to 0.88 for AU 12; according to convention, facial wrinkles, folds, and bulges). It is robust to changes in these numbers can be considered strong to very strong re- illumination and shares properties with neurons responsible liability (Chung, 2007). This high degree of inter-observer for object recognition in primate vision (Serre et al., 2005).
reliability is likely due to extensive training and supervision SIFT feature extraction was implemented using the VLFeat of the coders.
open-source library (Vedali & Fulkerson, 2008). The diame-ter of the SIFT descriptor was set to 24 pixels, as illustrated Automatic FACS Coding above the left lip corner in Figure 2.
The final step in automatically de- Figure 2 shows an overview of the AU detection pipeline.
tecting AUs was to train a classifier to detect each AU using The face is detected automatically and facial landmarks are SIFT features. By providing each classifier multiple exam- detected and tracked. The face images and landmarks are ples of an AU's presence and absence, it was able to learn normalized to control for variation in size and orientation, a mapping of SIFT features to that AU. The classifier then and appearance features are extracted. The features then are extrapolated from the examples to predict whether the AU input to classification algorithms, as described below. Please was present in new images. This process is called super- note that the mentioned procedures do not provide incremen- vised learning and was accomplished using support vector tal results; all the procedures are required to perform classi- machine (SVM) classifiers (Vapnik, 1995). SVM classifiers fication and calculate an inter-system reliability score.
extrapolate from examples by fitting a hyperplane of maxi- FACIAL EXPRESSION CAN BE MEASURED AUTOMATICALLY mum margin into the transformed, high dimensional feature detector), and prevents an undesired handicap from being in- space. SVM classification was implemented using the LIB- troduced by invariance to linear transformation. For exam- LINEAR open-source library (Fan, Wang, & Lin, 2008).
ple, an automated system that always detected a base rate The performance of a classifier is evaluated by testing the twice as large as that of the human coder would have a per- accuracy of its predictions. To ensure generalizability of the fect Pearson Correlation Coefficient, but a poor ICC. For this classifiers, they must be tested on examples from people they reason, the behavior of ICC is more rigorous than that of the have not seen previously. This is accomplished by cross- Pearson Correlation Coefficient when applied to continuous validation, which involves multiple rounds of training and values. We used the one-way, random effects model ICC testing on separate data. Stratified k-fold cross-validation described in Equation 1.
(Geisser, 1993) was used to partition participants into 10folds with roughly equal AU base rates. On each round ofcross-validation, a classifier was trained using data (i.e., fea- √(TP + FP)(TP + FN)(TN + FP)(TN + FN) tures and labels) from eight of the ten folds. The classifier's cost parameter was optimized using one of the two remainingfolds through a "grid-search" procedure (Hsu, Chang, & Lin, The Matthews Correlation Coefficient (MCC), also known 2003). The predictions of the optimized classifier were then as the phi coefficient, can be used as a measure of the quality tested through extrapolation to the final fold. This process of a binary classifier (D. M. Powers, 2007). It is equivalent was repeated so that each fold was used once for testing and to a Pearson Correlation Coefficient computed for two binary parameter optimization; classifier performance was averaged measures and can be interpreted in the same way: an MCC over these 10 iterations. In this way, training and testing of of 1 indicates perfect correlation between methods, while an the classifiers was independent.
MCC of 0 indicates no correlation (or chance agreement).
MCC is related to the chi-squared statistic for a 2×2 con- Inter-system Reliability tingency table, and is the geometric mean of Informedness The performance of the automated FACS coding system (DeltaP) and Markedness (DeltaP'). Using Equation 2, MCC was measured in two ways. Following the example of Girard, can be calculated directly from a confusion matrix. Although Cohn, Mahoor, Mavadati, Hammal, and Rosenwald (2013), there is no perfect way to represent a confusion matrix in a we measured both session-level and frame-level reliability.
single number, MCC is preferable to alternatives (e.g., the Session-level reliability asks whether the expert coder and F-measure or Kappa) because it makes fewer assumptions the automated system are consistent in their estimates of the about the distributions of the data set and the underlying pop- proportion of frames that include a given AU. Frame-level ulations (D. M. W. Powers, 2012).
reliability represents the extent to which the expert coder and Because ICC and MCC are both correlation coefficients, the automated system make the same judgments on a frame- they can be evaluated using the same heuristic, such as the by-frame basis. That is, for any given frame, do both detect one proposed by Chung (2007): that coefficients between 0.0 the same AU? For many purposes, such as comparing the and 0.2 represent very weak reliability, coefficients between proportion of positive and negative expressions in relation to 0.2 and 0.4 represent weak reliability, coefficients between severity of depression, session-level reliability of measure- 0.4 and 0.6 represent moderate reliability, coefficients be- ment is what matters. Session-level reliability was assessed tween 0.6 and 0.8 represent strong reliability, and coefficients using intraclass correlation (ICC) (Shrout & Fleiss, 1979).
between 0.8 and 1.0 represent very strong reliability.
Frame-level reliability was quantified using the MatthewsCorrelation Coefficient (MCC) (D. M. Powers, 2007).
We considered a variety of factors that could potentially influence automatic AU detection. These were participant BMS + (k − 1)W MS gender, ethnicity, mean pixel intensity of the face, seatinglocation, and variation in head pose. Mean pixel intensity The Intraclass Correlation Coefficient (ICC) is a measure is a composite of several factors that include skin color, ori- of how much the units in a group resemble one another entation to overhead lighting, and head pose. Orientation to (Shrout & Fleiss, 1979). It is similar to the Pearson Cor- overhead lighting could differ depending on participants' lo- relation Coefficient, except that for ICC the data are centered cation at the table. Because faces look different when viewed and scaled using a pooled mean and standard deviation rather from different angles, pose for each frame was considered.
than each variable being centered and scaled using its own The influence of ethnicity, sex, average pixel intensity, mean and standard deviation. This is appropriate when the seating position, and pose on classification performance was same measure is being applied to two sources of data (e.g., evaluated using hierarchical linear modeling (HLM; Rau- two manual coders or a manual coder and an automated AU denbush & Bryk, 2002). HLM is a powerful statistical tool JEFFREY M. GIRARD for modeling data with a "nested" or interdependent struc- Session-level Reliability (ICC) Frame-level Reliability (MCC) ture. In the current study, repeated observations were nestedwithin participants. By creating sub-models (i.e., partition- ing the variance and covariance) for each level, HLM ac-counted for the fact that observations from the same partic- ipant are likely to be more similar than observations from Classifier predictions for each video frame were assigned a value of 1 if they matched the manual coder's annotationand a value of 0 otherwise. These values were entered into a two-level HLM model as its outcome variable; a logit- link function was used to transform the binomial values into Figure 3. Mean inter-system reliability for twelve AUs continuous log-odds. Four frame-level predictor variableswere added to the first level of the HLM: z-scores of eachframe's head pose (yaw, pitch, and roll) and mean pixel in- p < 0.001. Effects of seating location were also significant, tensity. Two participant-level predictor variables were added with participants sitting in one of the chairs showing signifi- to the second level of the HLM: dummy codes for participant cantly lower mean pixel intensity than participants sitting in gender (0=male, 1=female) and ethnicity (0=White, 1=Non- the other chairs, F(79) = 5.71, p < .01. Head pose was white). A sigmoid function was used to transform log-odds uncorrelated with pixel intensity: for yaw, pitch, and roll, to probabilities for ease of interpretation.
r = −0.09, −0.07, and −0.04, respectively.
Inter-System Reliability The mean session-level reliability (i.e., ICC) for AUs was Descriptive Statistics very strong at 0.89, ranging from 0.80 for AU 17 to 0.95 for Using manual FACS coding, the mean base rate for AUs AU 12 and AU 7 (Fig. 3). The mean ICC was 0.91 for male was 27.3% with a relatively wide range. AU 1 and AU 15 participants and 0.79 for female participants. The mean ICC were least frequent, with each occurring in only 9.2% of was 0.86 for participants self-identifying as White and 0.91 frames; AU 12 and AU 14 occurred most often, in 34.3% for participants self-identifying as Nonwhite.
and 63.9% of frames, respectively (Table 1). Occlusion, de- The mean frame-level reliability (i.e., MCC) for AUs was fined as partial obstruction of the view of the face, occurred strong at 0.60, ranging from 0.44 for AU 15 to 0.79 for AU in 18.8% of all video frames.
12 (Fig. 3). The mean MCC was 0.61 for male participants Base rates for two AUs differed between men and women.
and 0.59 for female participants. The mean MCC was 0.59 Women displayed significantly more AU 10 than men, for participants self-identifying as White and 0.63 for partic- t(78) = 2.79, p < .01, and significantly more AU 15 than ipants self-identifying as Nonwhite.
men, t(78) = 3.05, p < .01. No other significant differences between men and women emerged, and no significant differ-ences in base rates between Whites and Nonwhites emerged.
HLM found that a number of participant- and frame- Approximately 5.6% of total frames could be coded man- level factors affected the likelihood that the automated sys- ually but not automatically. 9.7% of total frames could be tem would make classification errors for specific AUs (Ta- coded neither automatically nor manually. Occlusion was ble 2). For several AUs, participant gender and self-reported responsible for manual coding failures. Tracking failure was ethnicity affected performance. Errors were 3.45% more responsible for automatic coding failures.
likely in female than male participants for AU 6 (p < .05), Head pose was variable, with most of that variation occur- 2.91% more likely in female than male participants for AU ring within the interval of 0 to 20◦ from frontal view. (Abso- 15 (p < .01), and 5.15% more likely in White than Non- lute values are reported for head pose.) Mean pose was 7.6◦ white participants for AU 17 (p < .05). For many AUs, for pitch, 6.9◦ for yaw, and 6.1◦ for roll. The 95th percentiles frame-level head pose and mean pixel intensity affected per- were 20.1◦ for pitch, 15.7◦ for yaw, and 15.7◦ for roll.
formance. For every one standard deviation increase in the Although illumination was relatively consistent in the ob- absolute value of head yaw, the probability of making an er- servation room, the average pixel intensity of faces did vary.
ror increased by 0.79% for AU 2 (p < .05), by 0.15% for AU Mean pixel intensity was 40.3% with a standard deviation of 11 (p < .05), by 1.24% for AU 12 (p < .01), by 1.39% for 9.0%. Three potential sources of variation were considered: AU 23 (p < .05), and by 0.77% for AU 24 (p < .05). For ethnicity, seating location, and head pose. Mean pixel inten- every one standard deviation increase in the absolute value of sity was lower for Nonwhites than for Whites, t(78) = 4.87, head pitch, the probability of making an error increased by FACIAL EXPRESSION CAN BE MEASURED AUTOMATICALLY Table 1Action Unit Base Rates from Manual FACS Coding (% of frames) Note: Shaded cells indicate significant differences between groups (p < .05).
1.24% for AU 15 (p < .05). No significant effects were found ies of group formation (Fairbairn, Sayette, Levine, Cohn, & for deviations in head roll. Finally, for every one standard Creswell, 2013; Sayette et al., 2012) as well as emotion and deviation increase in mean pixel intensity, the probability of social interaction more broadly (Ekman & Rosenberg, 2005).
making an error increased by 2.21% for AU 14 (p < .05).
Session-level reliability for AUs related to brow actions andsmile controls, which counteract the upward pull of the zygo- matic major (Ambadar et al., 2009; Keltner, 1995), were onlysomewhat lower. Smile controls have been related to embar- The major finding of the present study was that sponta- rassment, efforts to down-regulate positive affect, deception, neous facial expression during a three person, unscripted so- and social distancing (Ekman & Heider, 1988; Girard, Cohn, cial interaction can be reliably coded using automated meth- Mahoor, Mavadati, & Rosenwald, 2013; Keltner & Buswell, ods. This represents a significant breakthrough in the field of 1997; Reed et al., 2007).
affective computing and offers exciting new opportunities for The more demanding frame-level reliability (i.e., MCC) both basic and applied psychological research.
averaged 0.60, which can be considered strong. Similar to We evaluated the readiness of automated FACS coding for the session-level reliability results, actions associated with research use in two ways. One was to assess session-level re- positive affect had the highest frame-level reliability (0.76 liability: whether manual and automated measurement yield for AU 6 and 0.79 for AU 12). MCC for smile controls was consistent estimates of the proportion of time that different more variable. For AU 14 (i.e., dimpler), which is associated AUs occur. The other, more-demanding metric was frame- with contempt and anxiety (Fairbairn et al., 2013), and AU level reliability: whether manual and automated measure- 10, which is associated with disgust (Ekman, 2003), reliabil- ment agree on a frame-by-frame basis. When average rates ity was strong (MCC = 0.60 and 0.72, respectively). MCC of actions are of interest, session-level reliability is the crit- for some others was lower (e.g., 0.44 for AU 15). When ical measure (e.g., Sayette & Hufford, 1995; Girard, Cohn, frame-by-frame detection is required, reliability is strong for Mahoor, Mavadati, Hammal, & Rosenwald, 2013). When some AUs but only moderate for others. Further research it is important to know when particular actions occur in the is indicated to improve detection of the more difficult AUs stream of behavior, for instance to define particular combi- (e.g., AU 11 and AU 15).
nations of AUs, frame-level reliability is what matters (e.g., Our findings from a demanding group formation task with Ekman & Heider, 1988; Reed, Sayette, & Cohn, 2007). For frequent changes in head pose, speech, and intensity are AUs that occurred as little as 3% of the time, we found evi- highly consistent with what has been found previously in dence of very strong session-level reliability and moderate to more constrained settings. In psychiatric interview, for in- strong frame-level reliability. AUs occurring less than 3% of stance, we found that automated coding was highly con- the time were not analyzed.
sistent with manual coding and revealed the same pattern Session-level reliability (i.e., ICC) averaged 0.89, which of state-related changes in depression severity over time can be considered very strong. The individual coefficients (Girard, Cohn, Mahoor, Mavadati, Hammal, & Rosenwald, were especially strong for AUs associated with positive af- fect (AU 6 and AU 12), which is of particular interest in stud- Results from error analysis revealed that several JEFFREY M. GIRARD Table 2Standardized Regression Coefficients Predicting the Likelihood of Correct Automated Annotation Participant Variables Video Frame Variables Note: Standardized regression coefficients are in log-odds form. ∗ = p < .05 and ∗∗ = p < .01 participant-level factors influenced the probability of mis- be expected in the context of a spontaneous social interac- classification. Errors were more common for female than tion. For contexts in which larger pose variation is likely, male participants for AU 6 and AU 15, which may be due pose-dependent training may be needed (Guney, Arar, Fis- to gender differences in facial shape, texture, or cosmetics- cher, & Ekenel, 2013). Although the effects of mean pixel in- usage. AU 15 was also more than twice as frequent in female tensity were modest, further research is needed in databases than male participants, which may have led to false nega- with more variation in illumination.
tives for females. With this caveat in mind, the overall find- Using only a few minutes of manual FACS coding each ings strongly support use of automated FACS coding in sam- from 80 participants, we were able to train classifiers that ples with both genders. Regarding participant ethnicity, er- repeatedly generalized (during iterative cross-validation) to rors were more common in White than Nonwhite participants unseen portions of the data set, including unseen participants.
for AU 17. This finding may suggest that the facial texture This suggests that the un-coded portions of the data set - over changes caused by AU 17 are easier to detect on darker skin.
30 minutes of video from 720 participants - could be auto- Replication of this finding, however, would be important as matically coded via extrapolation with no additional manual the number of Nonwhite participants was small relative to coding. Given that it can take over an hour to manually code the number of White participants (i.e., 12 Nonwhite vs. 68 a single minute of video, this represents a substantial savings of time and opens new frontiers in facial expression research.
Several frame-level factors also influenced the probabil- A variety of approaches to AU detection using appearance ity of misclassification. In the group formation task, most features have been pursued in the literature. One is static head pose variation was within plus or minus 20◦ of frontal modeling; another is temporal modeling. In static modeling, and illumination was relatively consistent. Five AUs showed each video frame is evaluated independently. For this reason, sensitivity to horizontal change in head pose (i.e., yaw): the it is invariant to head motion. Static modeling is the approach probability of errors increased for AU 2, AU 11, AU 12, AU we used. Early work used neural networks for static mod- 23, and AU 24 as participants turned left or right and away eling (Tian, Kanade, & Cohn, 2001). More recently, sup- from frontal. Only one AU showed sensitivity to vertical port vector machine classifiers such as we used have pre- change in head pose (i.e., pitch): the probability of errors dominated (De la Torre & Cohn, 2011). Boosting, an iter- increased for AU 15 as participants turned up or down and ative approach, has been used to a lesser extent for classifi- away from frontal. No AUs showed sensitivity to rotational cation as well as for feature selection (G. Littlewort, Bartlett, change in head pose (i.e., roll). Finally, only one AU showed Fasel, Susskind, & Movellan, 2006; Zhu, De la Torre, Cohn, sensitivity to change in illumination: the probability of errors & Zhang, 2011). Others have explored rule-based systems increased for AU 14 as mean pixel intensity increased. These (Pantic & Rothkrantz, 2000) for static modeling. In all, static findings suggest that horizontal motion is more of a concern modeling has been the most prominent approach.
than vertical or rotational motion. However, the overall relia- In temporal modeling, recent work has focused on incor- bility results suggest that automated FACS coding is suitable porating motion features to improve performance. A popular for use in databases with the amount of head motion that can strategy is to use hidden Markov models (HMM) to tempo- FACIAL EXPRESSION CAN BE MEASURED AUTOMATICALLY rally segment actions by establishing a correspondence be- others have proposed using either the distance measure or tween AU onset, peak, and offset and an underlying latent a pseudo-probability based on that distance measure. This state. Valstar and Pantic (2007) used a combination of SVM method worked well for posed facial actions but not for spon- and HMM to temporally segment and recognize AUs. In sev- taneous ones (Bartlett et al., 2006; Girard, 2014; Yang, Qing- eral papers, Qiang and his colleagues (Li, Chen, Zhao, & Ji, shan, & Metaxas, 2009). To automatically measure intensity 2013; Tong, Chen, & Ji, 2010; Tong, Liao, & Ji, 2007) used of spontaneous facial actions, we found that it is necessary what are referred to as dynamic Bayesian networks (DBN) to train classifiers on manually coded AU intensity (Girard, to detect facial action units. DBN exploits the known cor- 2014). In two separate data sets, we found that classifiers relation between AU. For instance, some AUs are mutually trained in this way consistently out-performed those that re- exclusive. AU 26 (mouth open) cannot co-occur with AU lied on distance measures. Behavioral researchers are cau- 24 (lips pressed). Others are mutually "excitatory." AU 6 tioned to be wary of approaches that use distance measures and AU 12 frequently co-occur during social interaction with in such a way.
friends. These "dependencies" can be used to reduce uncer- Because classifier models may be sensitive to differences tainty about whether an AU is present. While they risk false in appearance, behavior, context, and recording environment positives (e.g., detecting a Duchenne smile when only AU 12 (e.g., cameras and lighting), generalizability of AU detection is present), they are a promising approach that may become systems from one data set to another cannot be assumed. A more common (Valstar & Pantic, 2007).
promising approach is to personalize classifiers by exploit- The current study is, to our knowledge, the first to per- ing similarities between test and training subjects (Chu, De form a detailed and statistically-controlled error analysis of la Torre, & Cohn, 2013; Chen, Liu, Tu, & Aragones, 2013; an automated FACS coding system. Future research would Sebe, 2014). For instance, some subjects in the test set may benefit from evaluating additional factors that might influ- have similar face shape, texture, or lighting to subsets of sub- ence classification, such as speech and AU intensity. The jects in the training. These similarities could be used to op- specific influence of speech could not be evaluated because timize classifier generalizability between data sets. Prelimi- audio was recorded using a single microphone and it was not nary work of this type has been encouraging. Using an ap- feasible to code speech and non-speech separately for each proach referred to as a selective transfer machine, Chu et al.
participant. The current study also focused on AU detection (2013) achieved improved generalizability between different and ignored AU intensity.
data sets of spontaneous facial behavior.
Action units can vary in intensity across a wide range from In summary, we found that automated AU detection can subtle, or trace, to very intense. The intensity of facial ex- be achieved in an unscripted social context involving spon- pressions is linked to both the intensity of emotional expe- taneous expression, speech, variation in head pose, and in- rience and social context (Ekman, Friesen, & Ancoli, 1980; dividual differences. Overall, we found very strong session- Hess, Banse, & Kappas, 1995; Fridlund, 1991), and is essen- level reliability and moderate to strong frame-level reliabil- tial to the modeling of expression dynamics over time. In an ity. The system was able to detect AUs in participants it had earlier study using automated tracking of facial landmarks, never seen previously. We conclude that automated FACS we found marked differences between posed and sponta- coding is ready for use in research and applied settings, neous facial actions. In the former, amplitude and velocity of where it can alleviate the burden of manual coding and en- smile onsets were strongly correlated consistent with ballistic able more ambitious coding endeavors than ever before pos- timing (Cohn & Schmidt, 2004). For posed smiles, the two sible. Such a system could replicate and extend the exciting were uncorrelated. In related work, Messinger et al. (2009) findings of seminal facial expression analysis studies as well found strong covariation in the timing of mother and infant as open up entirely new avenues of research.
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