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Reconsidering Facial Expressions and Deception Detection
The function of facial expressions of emotions in detecting deception has been a hotly
debated topic. One side argues that liars and truth-teller display different facial expressions
which can be used as diagnostic cues of deceit. The other argues that such cues are rare,
unpredictable, and ambiguous, and as such are unreliable to detecting deception. This chapter
overviews facial expression in deception detection, separating their alleged diagnostic value
as cues to deception from that of strategic affective signals in human communication.
Building upon our current understanding and research in the deception and emotion fields, I
elaborate on relevant but underdeveloped concepts, and address how the process of detecting
lies can be influenced by facial expressions of emotions. I critically evaluate several
assumptions of the emotion-based approach to detecting deception, illustrating the limitations
of this view. A strong emphasis is placed on expanding the role of facial expressions in
deception, by considering both the encoder-decoder and the affective-signaling perspectives.
I propose a careful distinction between genuine cues and deceptive cues, considering the
importance of emotional authenticity and sender intent. Finally, I consider the role of facial
expressions of emotion in human veracity judgment and future directions for the field of
emotion and deception in light of the current propositions. This is done in light of recent
propositions to the use of automated lie detection tools on the basis of facial expressions of
emotion. I argue that caution must be given to such techniques, elaborating on the flawed
underpinnings guiding their decisions, and make considerations for the future of this research.
Department of Criminology and Sociology, Kingston University London, Kingston upon
Word count: 7,183
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Lying is a common and even necessary component of social interaction (DePaulo, Kashy,
Kirkendol, Wyer, & Epstein, 1996), however, it is surprisingly difficult to detect (Bond &
DePaulo, 2006). Scholars have spent great effort understanding deception and its detection,
producing an impressive body of research (for an overview, see Vrij, 2008). Beyond the
theoretical interest in understanding human social interactions, identification of deception is
crucial for many professions. This is particularly evident in the security domains where
wrongful incriminations or incorrect exonerations can have severe consequences (Kassin,
2012). Similarly, in the medical and psychiatric sectors, not detecting patient malingering or
concealment can have life-threatening consequences (Rogers & Gillard, 2013).
Although there exist many propositions for the poor accuracy of detecting lies, one
important factor is the lack of diagnostic cues of deceit (DePaulo et al., 2003). Thus far, the
deception literature has not found any unique behavior that systematically relates to deception
(i.e. the proverbial Pinocchio’s nose). That said, there are several prevalent beliefs, both in
academic circles and popular media, of cues relating to deception; although, many such
beliefs are incorrect (Bogaard, Meijer, Vrij, & Merckelbach, 2016; The Global Deception
Research Team, 2006). One purported source of such “cue of deception” is the facial
expressions of liars and truth-tellers.
Facial expressions of emotions have long been suggested to be strongly related to
deception and its detection. However, a decade of empirical research has not supported this
claim (DePaulo et al., 2003; Hartwig & Bond, 2014). Indeed, many psychologists have
moved away from research in this area toward more cognitive approaches to detecting lies
(Sporer, 2016). This move, I argue, is premature and neglects the role of emotions in human
veracity judgments. Moreover, the void created has slowly filled with computer science
research focused on automated facial expressions recognition techniques (e.g., Goh, Ng, Lim,
& Sheikh, 2018; Kulkarni et al., 2018), in seeming ignorance and/or opposition to the
criticism of such approaches (Cooney, Pashami, Sant’Anna, Fan, & Nowaczyk, 2018; Jupe &
Keatley, 2019). Nevertheless, while I agree with the issues raised against automated methods
of lie detection, or in general, the premise that facial expressions have a 1-to-1 relationship
with underlying affect (see Gunnery & Hall, 2014), I take a more tempered approach. I aim to
explain why the current conceptualization of emotions in deception—particularly of facial
expressions—is flawed, expound on the problems of current implementations, and propose
This chapter provides an overview and critical evaluation of the assumption
underpinning the emotion-based approach (EBA) for detecting deception. I reflect on the role
of facial expressions in the process of deception, considering both cue authenticity (genuine
and deceptive) and intent (involuntary or strategic). In contrast to the dominant view, I
consider the mixed and often contradictory findings in the literature to be in part attributable
to the narrow role assigned to facial expressions in communication. Finally, I propose a more
comprehensive conceptualization of facial expressions in deception and emotion research,
considering both theoretical issues and real-world applications.
THE EMOTION-BASED APPROACH
Underpinning the contentious link between facial expressions of emotion and deception
detection is the fact that even within the emotion literature there is no consensus on the
RECONSIDERING FACIAL EXPRESSIONS AND DECEPTION DETECTION 3
definition of an emotion (Izard, 2007). The fundamental concepts of what is an emotion, how
many there are, or how an emotion can be recognized is still under fierce debate.
A detailed exploration of this topic is beyond the scope of this chapter, but, a general
conceptualization is that emotions are physiological processes, with specific action
tendencies and subjective experiences (Lazarus, 1991). For deception, emotions are relevant
due to the alleged behavioral cues generated by the feelings experienced by the liar. Emotions
are a precursor (Moran & Schweitzer, 2008) and consequence (Ruedy, Moore, Gino, &
Schweitzer, 2013) of the act of deception, influencing the type of lie told or even the decision
to lie (e.g., Gaspar & Schweitzer, 2013).
To understand the issues surrounding emotions, facial expressions, and deception
detection, one needs to understand the theories that have informed the EBA. I overview the
dominant emotion theory and argue for why this view has, and continues to have, a
detrimental impact on research. The following sections focus on the EBA to deception
detection, while also addressing other relevant and not mutually exclusive approaches.
Universality and Basic Emotions
A longstanding debate in the emotion literature is the concept of universal emotions: the
supposition that humans share a set of innate, basic emotions with corresponding discrete
behavioral displays. A basic emotion is viewed as a set of specific neural, bodily, and
motivational components generated rapidly, automatically, and nonconsciously when
ongoing affective–cognitive processes interact with the sensing of an ecologically valid
stimulus to activate evolutionarily adapted neurobiological and mental processes. The
resulting basic emotion pre-empts consciousness and drives narrowly focused stereotypical
response strategies to achieve an adaptive advantage (Ekman, 1994; Izard, 2007).
Evidence for the discrete emotions account originated from neuropsychological
studies on patients with focal brain damage, such as the role of the amygdala in fear
responses (Adolphs et al., 2005), orbitofrontal cortex in anger (Berlin, Rolls, & Kischka,
2004), or the insula in disgust (Calder, Keane, Manes, Antoun, & Young, 2000). However,
more recent neuroimaging studies report more mixed results, some in favor of discrete neural
activity for specific emotions (Phan, Wager, Taylor, & Liberzon, 2002; Vytal & Hamann,
2010), while others finding overlapping activity (Murphy, Nimmo-Smith, & Lawrence,
This view of universal, discrete, or basic emotions has been at the foundation of the
EBA. I will explore this matter in the next section in more detail, especially as it pertains to
facial expressions. For now, I will highlight some of the issues with this approach, allowing
for a more comprehensive view to be presented.
In psychology, it is very difficult to argue that something is evolutionary universal.
Without exception, the universality hypothesis has been challenged in its ability to explain
real-world phenomena. As mentioned, there are still active debates regarding the exact
definition of an emotion (Ortony & Turner, 1990), the valence of specific emotions, and even
a taxonomy of all emotions (see Lindquist, Siegel, Quigley, & Barrett, 2013). Some scholars
challenge the concept of discrete emotions and basic expressions, proposing a valence-
arousal dimension approach, arguing that emotions are also strongly influenced by culture,
experience, and learning (see Barrett, 2006).
Such critics argue that past research on universality is flawed due to its use of dubious
methodologies, such as forced-choice paradigms (i.e. providing pre-selected labels and
asking participants to group facial expressions), or artificial stimuli (e.g., highly intense,
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prototypical expressions, presented in isolation), and an overreliance on westernized samples.
For example, recent work using indigenous societies failed to find support for universality
(Crivelli, Jarillo, Russell, & Fernández-Dols, 2016; Gendron, Roberson, van der Vyver, &
Barrett, 2014), while a review of 57 datasets found that only a few emotional expression
(namely, happiness and surprise) were cross-culturally recognized (Nelson & Russell, 2013).
In favor of universality, a meta-analysis conducted on 1,500 articles on emotions supported
the discrete emotions hypothesis (Lench, Flores, & Bench, 2011), although this finding, also,
has been criticized (Lindquist et al., 2013), and rebutted (Lench, Bench, & Flores, 2013).
Furthermore, even findings in favor of universality report cultural differences in both
presentation and recognition (Elfenbein & Ambady, 2002; however, see Matsumoto, 2002).
Fear, Guilt, and Duping Delight
There are three primary emotions argued to relate to lying: fear, guilt, and duping delight
(Ekman, 2009). These emotions are said the be experienced by individuals when they lie,
resulting in behavioral differences between them and truth-tellers. Evidently, this perspective
is strongly rooted in the belief of a direct link between underlying affect and behavioral
In a lying scenario, fear is said to be experienced as a response to detection
apprehension (Ekman & Frank, 1993), the feeling that your deception will be uncovered. The
experience of fear is modulated by the situation and the liars’ perception of the situation. For
instance, if the liar is taking part in a police interview (i.e. a high-stakes situations) they may
experience stronger feelings of fear (Vrij, 2008); conversely, if they are confident in their
lying abilities or perceive the interviewer as unskilled they may experience less fear (Ekman,
2009). However, fear is not unique to liars. Truth-tellers may also experience fear. Although
their fear would stem from a different belief, such as not being believed or being falsely
accused, the external behaviors will be similar, limiting the diagnosticity of fear-based cues.
The second emotion, guilt, is experienced by the liar due to either the act of deceiving
being perceived as a moral transgression or due to the reality of their own actions (DePaulo &
Kashy, 1998; Ekman, 2009). The experience of guilt can also be influenced by the type of lie
told. Lies of omissions or concealment are accompanied by reduced feelings of guilt
compared to fabrications (i.e. outright lies; Ekman, 2009; Vrij, 2008). Similarly, if a lie is
sanctioned by another or the liar believes the lie to be justified they will experience less guilt
The final emotion proposed is duping delight. This emotion differs from the previous
in terms of its positive valence. It reflects the excitement that a liar feels from “getting away”
with their deception and fooling their interrogator (Ekman, 2009). Duping delight has been
elaborated less well in the literature, and few have studied it empirically, however, research
supports that liars do experience more positive emotions than truth-tellers, finding that in
certain instances liars exhibit (i.e. leak) more facial expressions related to feeling happiness
(Porter, ten Brinke, & Wallace, 2012).
While this should not be taken as an exhaustive list of the potential emotions a liar
will experience or display, it severs to illustrate the rationale behind the EBA’s considerations
of veracity-based behavioral differences. Indeed, research investigating facial expressions of
liars and truth-tellers rarely find the above-mentioned emotions while finding additional
displays related to the liar’s attempts to use affect to “sell” their lies (e.g., ten Brinke &
RECONSIDERING FACIAL EXPRESSIONS AND DECEPTION DETECTION 5
The EBA’s assumptions rely heavily on the universality hypothesis, discrete emotions, and
the encoder-decoder perspective, arguing for the existence of involuntary, universal,
emotional cues to deceit. This posits that the emotions a liar experiences are too
overwhelming to successfully mask or suppress, resulting in emotional leakage (Ekman,
2003a). The strongest source of such emotional cues are facial expressions (Ekman, 2003a),
as liars seem unable to exert full control over their production (Hurley & Frank, 2011),
leaking genuine affect which can betray their lies (Porter & ten Brinke, 2008).
The inhibitions hypothesis is the foundation of the EBA and can be traced as far back as
Darwin (1872). Although its exact conceptualization has changed over the years, it is
generally argued to comprise of two complementary components. First, in intense emotional
scenarios facial muscle activity is involuntary and uncontrollable (i.e. leakage). Second,
emotional displays are a genuine reflection of a person’s underlying affect. It should be noted
that, while highly influential in academic circles and popular media, empirical support for
this hypothesis has been lacking.
FACIAL EXPRESSIONS OF EMOTION: CUES OR SIGNALS
Facial expressions are neurologically activated fixed and specific muscle patterns in response
to specific eliciting events (Tomkins, 1962), implying they are universal in nature (Ekman,
Friesen, & Hager, 1978). They occur spontaneously and do not require learning or experience
to produce “successfully” (i.e. innate reactions; Matsumoto & Willingham, 2009). These are
roughly defined as biological remnants of once-needed behaviors, that originally served the
purpose other than that of communication (i.e. they became communicative by association;
see Bachorowski & Owren, 2003).
The basis of the EBA is the assumption that telling a lie is associated with different
emotions than those experienced when being honest (DeTurck & Miller, 1985). As such, liars
will display different emotional behavior and reactions to being questioned than would truth-
tellers, referred to as emotional cues. This lies at the core of the encoder-decoder perspective
used in deception detection research, whereby emotional cues (such as facial expressions) are
treated as involuntary indicators of underlying affect which we can decoded by an observer
(i.e. decoder) to their benefit.
For emotion recognition, research has found that the recognition of basic
expressions—facial displays corresponding to specific emotions—is also innate, cross-
cultural (Ekman et al., 1987), and significantly higher than chance (81-95% accuracy;
Ekman, 2003b). This recognition is achieved quickly, efficiently and with minimal cognitive
effort (Tracy & Robins, 2008). However, different emotions produce different recognition
rates (e.g., Ekman & Friesen, 1971, 1986).
The EBA argues that the strongest and most reliable source of cues to deceit are
microexpressions (Ekman & Friesen, 1969). Microexpressions are split-second (0.5 seconds),
full-face expressions, which reflect the genuine emotional state of the sender (Frank &
Svetieva, 2015), resulting from failed attempts to mask or suppress one’s emotions (Ekman,
2003a). Microexpressions have been reported in laboratory (Ekman & Friesen, 1969; Frank
& Ekman, 1997) and real-world scenarios (Porter & ten Brinke, 2008, 2010). However,
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untrained decoders seem unable to use microexpressions to detect veracity (Ekman &
Oddly, the few empirical investigations of microexpressions focus on training
methods to improve their recognition, which do find positive results (Hurley, 2012;
Matsumoto & Hwang, 2011), while studies arguing for a link between microexpressions and
deception detection are correlational in nature only. From such studies, in specific scenarios,
individual differences in microexpression recognition accuracy have shown positive
correlations with deception detection, such as for emotional lies (Ekman & O’Sullivan, 1991)
and mock crimes (Frank & Ekman, 1997). However, in the real world “pure” and intense
expressions of an emotion are rare (ten Brinke & Porter, 2012), last longer than half a second,
appear as partial expressions, and occur during both deceptive and honest scenarios (Porter et
al., 2012). Furthermore, when measuring deception detection performance based on
microexpressions recognition and training experimentally no causal relationship with
accuracy is found (Jordan et al., 2019; Zloteanu, Bull, & Richardson, 2019). However, while
microexpressions may signal genuine emotions, they are rare, ambiguous, and not exclusive
to deceptive episodes, minimizing their diagnostic value as cues to deceit.
An often-overlooked concession to the encoder-decoder perspective of the EBA, which
considers the influence of culture, are display rules. These are specific cultural or societal
norms relating to the type and intensity of emotions that are allowed to be displayed (Ekman
& Friesen, 1971). Specific socially-learned mechanisms dictate how emotional displays are
managed, and how these can differ between cultures (Fischer & Manstead, 2008; Koopmann-
Holm & Matsumoto, 2011).
An important aspect of display rules is that they consider an interactive component.
That is, the sender only suppresses socially unacceptable expressions if they believe they are
being observed (Ekman et al., 1987). For example, the expression of shame is suppressed to a
greater extent in individualistic cultures, where it is perceived as a sign of social ridicule, than
in collectivist cultures, where it is perceived as a reflection of being humble (Tracy &
A more recent criticism for universality is the dialect theory of emotions (Elfenbein, Beaupré,
Lévesque, & Hess, 2007). While display rules focus on controlling which expressions are
appropriate to display given the culture, dialects reflect differences in expression production
and recognition (Elfenbein & Ambady, 2002). Deviating from the encoder-decoder
perspective, this view argues that facial expressions should be seen as communicative signals
instead of an evolutionary bi-product. Elfenbein, Beaupré, Lévesque, and Hess (2007)
reported two studies in which culture modulated the muscle activation related to specific
emotional expressions and their recognition by in-group and out-group members, finding in-
group production similarity and recognition superiority. In this view, emotional displays need
to be clear communicative signals, developing cultural differences allows for recognition
benefits among in-group members and impediments to out-group members.
The dialect theory too has been criticized, on two accounts. First, the research
underlying this perspective was conducted on posed facial expressions (i.e. voluntarily
produced emotional displays), and as such may not generalizable to spontaneous (naturally
occurring) displays (Matsumoto, Olide, & Willingham, 2009). Second, apparent cultural
RECONSIDERING FACIAL EXPRESSIONS AND DECEPTION DETECTION 7
differences can be explained by the frequency with which specific emotional expressions
occur in everyday life. Recognition differences are argued to emerge from experiences with
specific facial displays, as different cultures may have differences in the frequency with
which certain emotions are displayed (see Calvo, Gutiérrez-García, Fernández-Martín, &
Nevertheless, the above research illustrates that facial expressions can reflect both an
involuntary cue to be detected (i.e. encoder-decoder perspective) and a communicative signal
aimed at informing others (i.e. affective-signaling perspective).
Attempted Behavioral Control
The attempted behavioral control proposes that liars are aware of potential scrutiny during
their deception and attempt to monitor and control their behavior to reflect that of an honest
person (Buller & Burgoon, 1996). While this may seem like a sensible strategy, it has two
faults. First, liars must rely on stereotypical beliefs about the behavior of an “honest” person,
which tend to be incorrect (Bogaard et al., 2016). Second, they require the ability to reliably
reproduce such behaviors, which proponents of the EBA argue cannot be voluntarily done
(i.e. it will result in differences in appearance, fluidity, intensity, timing, etc.; Ekman, 2009b,
cf. Namba, Makihara, Kabir, Miyatani, & Nakao, 2016). Ironically, forcing honest-looking
behavior can produce behavioral differences that separate liars from truth-tellers.
Interpersonal Deception Theory
Interpersonal Deception Theory (IDT; Buller & Burgoon, 1996) proposes that deception and
its detection are dynamic in nature. In a face-to-face scenario, liars will monitor not only their
own behavior but also that of the receiver of their message, and the behavior of one will
affect the other. For example, if the liar believes the receiver is becoming suspicious they
may change the way they speak or behave in the hopes of winning back their trust (Burgoon,
Buller, Floyd, & Grandpre, 1996). IDT suggests the bidirectional nature of a deceptive
encounter can influence not only the behavior of the liar but also the behavior of the decoder
(Burgoon, Buller, White, Afifi, & Buslig, 1999).
The self-presentational perspective (DePaulo, 1992; DePaulo et al., 2003) assumes that all
forms of communication involve an aspect of self-presentation, implying that all individuals
attempt to control the way they are perceived by others. Under this formulation both liars and
truth-tellers are subject to the same cognitive and emotional pressures, resulting in similar
behavioral cues, although generated for different reasons (Bond & Fahey, 1987). This implies
that the behavioral cues used to detect deception might be less diagnostic, as they are shared
by both liars and truth-tellers.
The Illusion of Transparency
In opposition to the self-presentation perspective, research suggests that liars take their
credibility less for granted, while truth-tellers tend to believe that their innocence will “shine
through”. This belief is referred to as the illusion of transparency (Gilovich, Savitsky, &
Medvec, 1998). Ironically, this can result in truth-tellers appearing less credible. In general,
even truthful individuals can appear nervous, make mistakes, contradict themselves, and
forget details; resulting in inconsistencies in their stories. Additionally, truthful individuals
may become hostile towards their interviewer if they feel they are not believed, resulting in a
worse impression that that of a liar (Toris & DePaulo, 1984). Conversely, liars anticipate
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such issues by planning out their lies and attempting to be more “friendly” in their
interaction. Therefore, while liars manipulate their channels of communication, truth-tellers
ignore the importance of self-presentation, relying too much on their honesty being obvious.
This may result in behavioral differences being misconstrued or misclassified, biasing the
judgments made on the basis of such cues.
DETECTING LIES ON THE BASIS OF FACIAL EXPRESSIONS
The allure of the EBA is the purported universal nature of emotional cues, and the
generalizability of the knowledge relating to their detection as useful in all situations (Frank
& Ekman, 1997). Research on facial expressions has reported partial support for such claims.
Brief emotional displays (such as microexpressions) are present during deception, and seem
to be both involuntary and impossible to fully suppress (Hurley & Frank, 2011). Facial
expressions can predict behavior (Gottman, Levenson, & Woodin, 2001), and, at times,
predict veracity (Hartwig & Bond, 2011).
Initial support for the detectability of deception on the basis of facial expressions was
reported by Frank and Ekman (1997). By coding interview videos from a mock-crime
scenario, where half the participants were lying, the researchers were able to classify
statement veracity with 80% accuracy. They did so by the presence or absence of negative
emotions in the videos, as deceivers displayed more fear and disgust. Similar work by Porter
and ten Brinke (2008), using videos of real-life high-stakes deception, detailed the presence
of subtle emotional leakage occurring during falsified statements. A subsequent study by
Porter and colleagues (2012) found evidence of leakage in 98% of their senders at least once
(although, this occurred in both truthful and deceptive scenarios). They also reported that the
emotion influenced the amount of leakage, finding that fear and happiness accounted for the
most leakage. However, the facial cues detected accounted for only 2% of the events.
Moreover, the expressions found did not contain all the predicted facial muscles (i.e. partial
expressions), tended to co-occur with other emotional displays, and lasted longer than the
expected 0.5 seconds.
While these isolated findings may be taken as compelling evidence for deception
detection on the basis of facial expressions being possible, if not at least plausible, more
comprehensive investigations have not found such reliable patterns. Two large-scale meta-
analyses, considering 158 cues and 144 synthetized samples, have failed to find either that
facial cues are related to veracity (DePaulo et al., 2003), or that the accuracy for detecting
deception is related to the emotionality of the lie (Hartwig & Bond, 2014). Such
investigations considered multiple moderating and mediating factors, as well as the best-case
scenarios for detecting deception. As such, research overall shows little support for facial
expressions being reliable or valid indicators of deceit. Leaked facial expressions are rare,
ambiguous, non-prototypical, atheoretical, and not veracity-specific.
GENUINE AND DECEPTIVE FACIAL EXPRESSIONS
In the previous section, I discussed the issues with relying on facial expression to detect
deception, considering the traditional encoder-decoder perspective whereby the emotional
displays of a sender are a reliable and valid cue to their underlying affect. Under this view,
poor accuracy is due to a scarcity of cues available or the inability of the decoder to detect
such cues. However, this view is underlined by two assumptions that tend to be taken at face
value without scrutiny. These relate to the concept of reliable muscles (Ekman, 2003; Ekman
& Friesen, 1969). First, that there exist certain facial muscles which only activate during
RECONSIDERING FACIAL EXPRESSIONS AND DECEPTION DETECTION 9
genuine affect, referred to as reliable muscles. Second, that the voluntary activation or control
of these reliable facial muscles is impossible in the absence of felt affect.
Neuroanatomical research has indicated that there exist two separate neural pathways
related to the production of involuntary and voluntary facial expressions: the extrapyramidal
motor system (EMS) and the pyramidal motor system (PMS). The EMS relates to involuntary
facial expression production, characterized by reflex-like responses (Rinn, 1984).
Conversely, the PMS is activated during the production of voluntary facial expressions of
emotion, which is argued to result in differences in how they are produced (Ekman &
Friesen, 1982). These systems are usually referenced in support of behavioral differences
between voluntary and involuntary expressions.
The most well-known example of such a difference is the Duchenne (genuine) smile,
contrasted by the Non-Duchenne (polite) smile (Duchenne, 1862/1990). When an individual
experiences happiness it is said to activate specific facial muscles (the zygomatic major and
the orbicularis oculi), which cannot be voluntarily activated in the absence of genuine affect.
The belief that such clear demarcations between the physiognomy of genuine and deceptive
facial displays has led to the belief that there exist clear methods of discriminating emotional
authenticity. However, such claims have been called into question, with research
demonstrating that even voluntarily produced smiles can contain “reliable muscle” activation,
while genuine happiness displays can occur without activating all proposed reliable muscles
(Gosselin, Perron, & Beaupré, 2010; Gunnery, Hall, & Ruben, 2013; Krumhuber &
The irony should not be lost on the reader that many studies exploring differences
between genuine and deceptive facial expressions perceptions rely of stimuli produced by
actors under strict instructions (see Gunnery, Hall, & Ruben, 2013), as such questioning the
very notion of emotional authenticity. Indeed, emotion scholars are now proposing that we
stop ascribing such a clear 1-to-1 relationship between internal affective states and external
displays (Gunnery & Hall, 2014).
In order for facial expressions and deception detection to be properly understood,
researchers must consider that both genuine (i.e. involuntary) cues and deceptive (i.e.
voluntary) signals are relevant to the veracity judgment process. Considering behavioral cues
based on their authenticity, I propose, can explain a part of the inconsistencies in the EBA
literature. While liars may be unable to suppress all genuine emotional displays in highly
emotionally-charged situations (Ekman, Friesen, & O’Sullivan, 1988; Porter et al., 2012), this
does not preclude them from attempting to produce emotional displays to assist their lies.
Indeed, even proponents of the EBA suggest that liars can fake emotional expressions in the
absence of affect to support their lies (Ekman & Friesen, 1982). This brings us to the heart of
the current chapter, the issue of emotional cue authenticity.
DECEPTIVE EMOTIONAL CONTROL: STRATEGIC USE OF FACIAL EXPRESSIONS
Humans have evolved control over their facial muscles (Smith, 2004). Out of all nonverbal
channels, facial expressions are under most conscious control (Zuckerman, DePaulo, &
Rosenthal, 1986), allowing for complex displays (Willis & Todorov, 2006). Humans dedicate
significant attention to their own and others’ facial expressions, compared to all other
nonverbal channels (Noller, 1985). As such, to understand facial expressions in deception, a
more encompassing perspective is needed, integrating genuine and deceptive emotional cues.
Such a perspective must consider facial expressions as both genuine cues to underlying affect
and as deceptive signals used strategically by the sender.
10 M. ZLOTEANU
The dominant emotion-deception view used by many scholars and practitioners is that
of the EBA. However, this approach concerns itself primarily, if not exclusively, with
genuine cues. As established, this view does not hold up to empirical scrutiny. Considering
the deceptive signaling role of facial expressions will benefit both the theoretical models that
underpin emotions and deception and resolve part of the inconsistencies in the literature. The
authenticity dimension illustrates that the facial expression-based lie detection process should
have two components: (1) detect an emotional cue (i.e. classification accuracy), and (2)
determine if it is genuine or deceptive (i.e. authenticity discrimination). This first element
reflects the majority of the current literature. The second element has been largely
overlooked. I refer to this as deceptive emotional control (DEC).
Can Liars Produce Genuine-Looking Deceptive Facial Expressions?
An often overlooked aspect of facial expressions is that they are also under (partial) voluntary
control and are used for communication purposes (Mandal & Ambady, 2004). As I elaborated
in previous sections, there are multiple strategies that both liars and truth-tellers employ
during communication. It is reasonable to assume that facial expressions can be used
The EBA suggests that emotional control can appear in three primary ways: (i)
masking (i.e. replacing a felt expression with another emotional expression), (ii) suppressing
(i.e. maintaining a neutral face while experiencing a genuine emotion), and (iii) posing (i.e.
displaying an emotion absent underlying affect). However, producing such dishonest
emotional signals is believed to be difficult (Ekman et al., 1988) and costly (Owren &
Bachorowski, 2003). Contrariwise, while the EBA argues that genuine-looking emotional
signals cannot be voluntarily produced (Ekman, Roper, & Hager, 1980), there is no
evolutionary reason why voluntary facial control would not have also evolved (Izard, 1994).
Primate research suggests that facial mobility is predicted by group size, suggesting it
evolved to serve a social function (Dobson, 2009). However, while it is difficult to argue that
emotional expressions came about as an adaptive signal, as the possibility remains that they
occurred accidentally, this does not preclude their usage as signaling mechanism. If they are
signals they should be controllable (e.g., displays rules; Ekman & Friesen, 1971) and
moderated by context (e.g., cultural dialects; Elfenbein et al., 2007), as signals require
receivers (see Dezecache, Mercier, & Scott-Phillips, 2013). Indeed, a growing body of
evidence suggests that individuals are adept at producing deceptive emotional displays.
Much of the literature and argumentation for liars being unable to produce genuine-
looking expressions of emotions hinge on the Duchenne smile. However, this belief has not
held to scrutiny. Krumhuber and Manstead (2009) investigated facial action unit activation in
situations where individuals were feeling genuine happiness or pretending. Contrary to the
expected clear demarcation based on authenticity, they reported that not all instances of
genuine happiness resulted in facial activation of the assumed “reliable muscles”, while
voluntary smiles could contain the hallmarks of a Duchenne smile (i.e. activation of the
reliable muscles). This finding is echoed in the work of Gosselin and colleagues (2010), who
demonstrated that individuals can voluntarily activate their facial muscles simply by
emulating videos of others doing so. In a comprehensive investigation of both static and
dynamic facial expressions of emotions, attempting to uncover morphological and temporal
differences between genuine and deceptive displays Namba, Makihara, Kabir, Miyatani, and
Nakao (2016) reported few reliable difference that could be used to determine facial
authenticity. Although they found some support for differences in genuine and deceptive
expressions of surprise (temporal) and happiness (morphological), they found no evidence of
RECONSIDERING FACIAL EXPRESSIONS AND DECEPTION DETECTION 11
such differences for any other emotion they investigated (namely, disgust, fear, anger, and
Such findings are in direct opposition to the EBA’s claim of clear and reliable
emotional markers of deception. In support of the current argument that such deliberate (i.e.
deceptive) displays serving a strategic use in communication, research has demonstrated that
people produce such displays in ecologically valid settings (i.e. real life). For instance,
Gunnery and Hall (2014) showed not only that there exists a sizeable minority of individuals
who can deliberately produce genuine-looking smiles, but that these individuals tend to be
perceived as more persuasive. This is mirrored in research on real-world scenarios, where
liars attempt to produce emotional displays to “sell” their lies (ten Brinke & Porter, 2012),
and offenders capable of controlling their affective displays are more convincing and more
likely to be successful at deceiving (Krokoszinski & Daniela Hosser, 2016; Porter, ten
Brinke, & Wilson, 2009).
This is not to say that there do not exist differences between genuine and deceptive
emotional expressions. Other sources may still reflect the difference between voluntarily
produced and involuntarily activated emotional displays. Here, I only attempt to illustrate the
complexity of adopting such a perspective and the current state of the research. At present
caution needs to be given to claims of emotional authenticity classification, inferring of
underlying affective intent, or deception detection on the basis of facial expressions of
emotion, such as those arising in the computer science domains (e.g., Del Líbano, Calvo,
Fernández-Martín, & Recio, 2018; Kulkarni et al., 2018).
Can Decoders Differentiate Genuine and Deceptive Facial Expressions?
Humans are very good at perceiving and utilizing facial expressions to understand another’s
thoughts, emotional state (Frank, Ekman, & Friesen, 1993), mood (Todorov, Mandisodza,
Goren, & Hall, 2005), and even intentions (Barrett, Todd, Miller, & Blythe, 2005), making
quick inferences based on even briefly presented facial expressions (Willis & Todorov,
2006). Importantly, laypersons believe facial expressions can betray deception and are more
likely to focus on these when determining the veracity of a statement (The Global Deception
Research Team, 2006).
This lay belief (which, is not empirically supported) is in alignment with the EBA’s
propositions. Namely, that veracity judgments made on the basis of facial expressions are
accurate, while suggesting that poor deception detection is due either to a scarcity of
(leakage) cues in specific scenarios, or the lack of relevant knowledge on the part of the
decoder. I argue that this perspective is flawed. The distinction in terms of emotional cue
authenticity is crucial, as it exemplifies how the recognition of such cues is only useful to
deception detection if the decoder can also classify their authenticity (see also Zloteanu,
2015). As such, the ability of a decoder (human or artificial) to detect the presence of an
emotional cue is irrelevant if they cannot also determine if the cue is genuine or not.
Although this perspective is not novel, it seems to have been largely ignored in the
deception detection literature. Indeed, some of the strongest proponents of the EBA report
that human decoders are poor at judging veracity based on emotional cues and determining
the authenticity of facial expressions (Ekman & O’Sullivan, 1991; Frank & Ekman, 1997;
Porter & ten Brinke, 2008; Porter et al., 2012). For example, Porter, ten Brinke, and Wallace
(2012) found that untrained observers could not discriminate between genuine and deceptive
expressions beyond chance performance. In their study, the presence of more leaked
expressions in the high-intensity condition also did not aid classification, suggesting that, for
human decoders, intensity of the emotion or amount of cues are not factors in detection
12 M. ZLOTEANU
performance. Similarly, in the second study of their seminal work, Frank and Ekman (1997)
found that unaided human decoders could not detect the veracity of the senders in the mock-
crime videos with above-chance accuracy, regardless of the differences in emotional content
between liars and truth-tellers.
In two recent exploration of genuine and deceptive displays, Zloteanu, Krumhuber,
and Richardson (2018, 2019) investigated how different types of deceptive expressions are
perceived by human decoders. Here, careful consideration was given to the concepts of
genuine and deceptive stimuli, focusing on the underlying affective state of the sender instead
of a predefined criterion for grouping facial displays. Their findings demonstrated that
decoders can be easily fooled by deceptive expressions of emotions and that producing such
deceptive displays can be a trivial task for senders. These findings are mirrored in the
emotion recognition literature. In their meta-analysis, Gunnery and colleagues (2013) found
that voluntary Duchenne smiles are generally perceived and rated more positively, authentic,
genuine-looking, and that individuals able to produce such displays are seen as more
attractive and trustworthy. Simply put, when asked to pose facial expressions without any
prior training, people (i.e. senders) can easily fool decoders with their performances
(Gosselin et al., 2010; Gunnery et al., 2013; Krumhuber, Likowski, & Weyers, 2014;
Krumhuber & Manstead, 2009).
This illustrates that emotional cue authenticity and the underlying intent of the sender
are important components of veracity judgments, which require more careful consideration
within research and practice. From the above enumerated research, a clear picture begins to
emerge, where low accuracy based on facial expression is not (only) attributable to a scarcity
of cues or lack of ability to detect them (i.e. the EBA perspective), but also due to the
apparent lack of differences between genuine and deceptive displays and the inability of
decoders to discriminate their authenticity (i.e. the DEC perspective).
COURSE-CORRECTING: THE FUTURE OF FACIAL EXPRESSION RESEARCH
The aim of the current chapter has been to elaborate on the complexity of emotions and facial
expression in the deception detection process. This was done to illustrate several important
problems and considerations as it pertains to the research moving forward. First, facial
expressions of emotions do not provide a good basis for deception detection. Second,
considering both the theoretical underpinning and empirical findings, the EBA does not seem
optimal or even valid as an approach, as the genuine cues present during deceptive (or
honest) episodes are scarce, non-prototypical, ambiguous, atheoretical, and vary in duration
and intensity. Third, emotion research does not support the assumption that internal affective
states have a clear and direct relationship with external emotional displays. As such,
inferences about affective intent, underlying emotions, or even emotional authenticity, cannot
be made on the basis of the presence or absence of facial expressions. Fourth, facial
expressions should be understood as both involuntary cues (i.e. innate responses to
appropriate stimuli) and as affective signals (i.e. voluntary cues produced with the intent to
communicate affective information). Fifth, classification tools (manual and automatic) that
rely on the EBA and the universal/discrete emotion account are flawed and can have severe
ramifications if implemented thoughtlessly. Sixth, more theoretical development and rigorous
methodologies are required in research on emotions and deception detection.
The belief in facial displays (such as microexpressions) as a source of deception
detection cues and the existence of a clear demarcation between genuine and deceptive
display has resulted in faulty methodological practices, which are at the core of the issue of
using facial expressions to detect deception. Such research relies on expressions preselected
RECONSIDERING FACIAL EXPRESSIONS AND DECEPTION DETECTION 13
based on specific muscle activations, which will effectively transform an authenticity
discrimination task into a classification tasks (i.e. stimuli grouping). This, I believe, is one of
the main reasons for the inconsistencies reported in the literature and the stagnation in
theoretical development on this matter. Here I argued, hopefully convincingly, that while
facial expressions of emotions may not be the “cues to deception” that the EBA proposes
them to be (namely, they are of little diagnostic value) they are still important for human
veracity judgments, and moving away from this research domain is premature and
I also draw attention to the increase in computer science researchers piloting and
implementing so-called “automated lie detection techniques” based on facial expressions,
which can have negative outcomes if not considered in the light of theory and empirical
findings. I argue that there is no clear and definitive 1-to-1 relationship between underlying
affect and facial expressions (Barrett, 2006), and more importantly, that people can produce
genuine-looking expressions of emotions, which can fool decoders (Zloteanu et al., 2018).
Given the current state of the literature, there is insufficient evidence of physiological or
temporal differences between genuine and deceptive expressions to justify relying on such
information to judge intent or veracity. The current perspective is important as much focus
has been placed on method of detecting deception on the basis of facial expressions, such as
police training programs (e.g., Inbau, Reid, Buckley, & Jayne, 2011), and automated tools.
For example, the Transportation Security Administration in the USA has made substantial
financial contributions to developing and utilizing a technique called Screening Passengers
by Observation Techniques (SPOT), largely influenced by the EBA and microexpression
research (Weinberger, 2010). As new technologies emerge proposing automatic expression
classification and deception detection, with the argumentation being that such tools are
capable of inferring intent of a person (see Salmanowitz, 2018), more careful consideration
and scrutiny needs to be given to the underlying theoretical concepts and research informing
such actions. Researchers advocating for the use of automated facial expressions tools for
deception detection purposes, some of which are already being piloted in vivo, need to
carefully consider the ramification of their claims. I argue that the underlying assumptions of
these tools are false, and any outcome from their use will be unreliable and potentially
harmful. Any such inferences would be highly questionable and could result in miscarriages
In a rudimentary sense, a distinction must be made when utilizing facial expressions
of emotions (both in human and machine decoding) on the basis of both authenticity—
genuine and deceptive—and intent—involuntary or strategic. However, caution should be
given to what is meant by authenticity and intent, as I am not referring to underlying veracity
(i.e. if the sender is attempting to be honest or deceptive). As explained in the self-
presentational perspective, even honest senders can use impression management to their
benefit. For example, an employee might consider a joke made by their boss to be amusing,
but not enough to make them laugh out loud, so they force a laugh to communicate a positive
affective state. This would be considered in the deception field as an exaggeration, and not an
outright lie. But, for the authenticity classification, such a behavioral cue would be in line
with the genuine feeling of the sender (i.e. genuine amusement), but it was produced
voluntarily (i.e. deceptive intent). Decoding this cue (e.g., a smile/laugh) would still provide
valuable information to the decoder, and in this case would also reflect the true intentions of
the sender. For deception, inauthentic cues are specific cues that the liar portrays in order to
facilitate in “selling” the lie, such as attempting to emulate (often unsuccessfully) the
behavior of honest senders. Liars may also attempt to use deceptive cues to either mask or
suppress their true affect. These considerations and expansions on the role of facial
14 M. ZLOTEANU
expressions in communication are what I consider are needed to reform the field. Such
reformulations and implementations will allow for new insights, improved theoretical
models, and move the research in a positive direction, specifically as to the role of emotional
information in communication.
In contrast to many current scholars, I consider ignoring the influence of emotions and
facial expressions on human veracity judgments to be detrimental to the fields of emotions
and deception. It is an undeniable fact that humans rely on and have strong beliefs about the
role of facial expressions for judging others, and detecting the veracity of their statements.
Ignoring the importance and role of emotional cues in the deception detection process will
lead to more damage than good.
Emotions play a complex role in deception detection. In contrast to the dominant view—the
EBA—facial expressions are not found to be a valid and reliable source of cues to deceit, on
either theoretical and empirical accounts. Genuine emotional cues (such as microexpressions)
occur much too rarely and in much too variable a fashion as to be useful in detecting
deception. Conversely, deceptive emotional cues, while underrepresented in research and
theory, provide insight into the issues surrounding traditional approaches. Considering a more
comprehensive role for facial expressions, by complementing the encoder-decoder
perspective (i.e. the EBA) with the affective-signaling perspective (i.e. the DEC), can provide
a positive direction for future research. In this view, people are not simply seen as “leaky
liars”, where an astute decoder (human or otherwise) can perceive and interpret their cues to
determine veracity, but also as strategic communicators using deceptive emotional cues to
support their lies. Many problems that have arisen in the field within the past years stem from
the uncertainty around fundamental concepts of emotions. We need to be aware of the
limitations of our understanding, and question openly and critically the fundamental
assumptions underpinning our approaches; for instance, that outward emotional displays (i.e.
facial expressions) do not always reflect genuine underlying affect, nor that there is a clear
relationship between such displays and discrete emotional experiences. Finally, caution needs
to be given to methods purporting the automated classification of emotions and the inferring
of underlying intent based on facial expressions. It is unclear, given all the unknowns in the
literature, how exactly these are meant to achieve their goals and the validity of their results.
For the future a change must occur in the way emotions research is conducted, and the way
deception detection utilizes and considers the role of emotions. Facial expressions clearly
have an important role in human communication and in judging veracity. There are still many
avenues of research and theory which merit further scientific inquiry.
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