# Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset
## 1 Introduction  
It is considered hard for humans to detect when someone is lying. [PERSON] [1] highlights five reasons to explain why it is so difficult for us: (1) during most of human history, there were smaller societies in which liars would have had more chances of being caught with worse consequences than nowadays; (2) children are not taught how to detect lies since even their parents want to hide some things from them; (3) people prefer to trust in what they are told; (4) people prefer not to know the real truth; and (5) people are taught to be polite and _not steal information that is not given_. However, it has been argued that it is possible for someone to learn how to detect lies in another person given sufficient feedback (e.g., that 50% of the time, that person is lying) and focusing on micro-expressions [1, 2]. Building from the above, the detection of deceptive behavior using facial analysis has been proved feasible using macro- and, especially, micro-expressions [3, 4, 5]. However, micro-expressions are difficult to capture at standard frame rates and, given that humans can learn how to spot them to perform lie detection, the same training might be used byliars to learn how to hide them. Thus, there has been interest in detecting facial patterns of deceptive behavior that might not be visible to the naked eye, such as the heat signature of the periodital [6] or perinasal region [7] in thermal imagery, which cannot be perceived by human vision. One of the crucial aspects to appropriately address lie-detection research is the availability of adequate datasets, which is one fundamental element of open innovation in accelerating current research, as opposed to closed or private datasets, which characterizes the opposite counterpart (closed innovation) [8]. Regardless of current progress, however, the acquisition of training and, especially, evaluation material for lie detection is still rather a challenging task, particularly regarding the necessity to gather ground truth, namely, to know whether a person is lying or not. The main difficulty arises because such knowledge is not useful if the scenario is naively simulated (e.g., it is not sufficient to instruct a person to simply tell a lie). Research on high-stakes lies suggests that deceptive behavior can depend heavily on the potential consequences for the liar [9]. Thus, researchers have attempted to create artificial setups that can convincingly reproduce situations where two factors converge: (1) there is a potential for truthful deceptive behavior; (2) we know when a lie takes place and when the recorded subjects are telling the truth. Most attempts so far have focused on interview scenarios in which the participants are _instructed to lie_[6; 7; 10], although it is hard to simulate a realistic setting for genuine deceptive behavior. Alternatively, some researchers have worked in collaboration with police departments, with the benefit of a scenario that, in many cases, is 100% realistic, as it is based on interviews of criminal suspects. However, the problem in this setting is the ground truth: it is not possible to rely on legal decision making [11], and even the validity of confessions has been questioned [12]. In contrast, in this paper, we explore an alternative scenario where participants are recorded while playing a competitive game in which convincingly lying to the opponent(s) produces an advantage. On one hand, participants are intrinsically motivated to lie convincingly. Importantly, given the knowledge of the game rules, we can accurately determine whether a given behavior is honest or deceptive. The use of card games can also benefit from the occurrence of unexpected events that produce genuine surprise situations for the potential liar, which has been highlighted as beneficial for lie detection scenarios [9]. Thus, the goals of this paper are twofold.