# Decoding Humor-Induced Amusement via Facial Expression Analysis: Toward Emotion-Aware Applications
## 1 Introduction  
Although defining the pleasures of life is a very subjective question, most humans would agree that a good laugh and the associated positive affect are hard to beat. This probably partly explains why millions of individuals intensely scroll through funny short videos on social media on a daily basis. Crucially, a good sense of humor, smiling, and mirthful daughter are thought to contribute to health and overall well-being [1, 2]. Repeated exposures to positive humor allows individuals to better cope with stressful events [3, 4, 5], and have long-term beneficial effects on depression, anxiety, and memory [6, 7, 8, 9, 10]. Despite the acknowledged benefits of humor and laughter for health, the difficulty in inducingand measuring humorous amusement in experimental contexts is an important issue for researchers and clinicians. The main challenges arise from the highly variable and subjective nature of humor perception [11], as well as the limited choice of stimuli that may have low personal relevance for participants [12; 13; 14; 15]. Although comedies are consistently--and expectedly--rated as funnier than horror or romance movies [16; 17], personality traits and environment significantly impact humor appreciation [18; 19; 20; 21; 22]. Furthermore, the comprehension-elaboration theory of humor [23] posits that inducing humor consists of two stages: comprehension and elaboration. Comprehension entails the identification and resolution of incongruuity, while elaboration involves the experience of amusement. These two stages introduce inherent fluctuations in the intensity of amusement, further complicating its assessment. To address these challenges, we aimed to develop an objective measure of amusement intensity induced by humorous video clips based on nonverbal emotional expressions and video features. Specifically, the Facial Action Coding System (FACS) has been commonly used to infer positive emotions from nonverbal behavior [24; 25]. The FACS involves transforming facial expressions into Action Units (AU), usually corresponding to a specific facial muscle, and separately rating them on a quantitative scale. The FACS has been proved useful to assess the latent physical traits of moods [26; 27] and distinguish between genuine Duchenne smiles, which involve movement of the orbicularis oculi muscle around the eye (AU6) and zygomatic major muscle at the corners of the mouth (AU12), and non-genuine smiles only involving AU12 [28; 29; 30; 31]. However, there is a controversy surrounding Duchenne smiles that could be feigned and wrongly detected when simply looking for eye constriction (AU6) [32; 33; 34]. This research suggests that more fine-grained methods may be necessary to accurately measure the dynamic and complex nature of humor perception. Some recent research has turned to machine learning to automate the identification of basic emotions [35] using facial expressions [36; 37; 38], as well as multimodal bio-signals such as speech [39] and physiological activity [40; 41]. Another approach to detect emotion relies on feature extraction from pictures and videos, including geometry [42; 43], art-features such as colors or contrasts and semantic annotations [44; 45; 46]. Such features can accurately distinguish comedies from action, dramas, and horror films [47], and be used to partially modulate the funniness of a scene [48]. While these methods have shown promising results in classifying films and pictures into emotional groups, they rarely target humor and do not predict the intensity of the emotions experienced by the viewer. Further research is needed to develop fine-grained methods to accurately measure the dynamic and complex nature of humor perception and its induced amusement intensity. The present study implements and validates a machine learning approach to predict the intensity of amusement evoked by humorous video content using both nonverbal emotional expressions and video features.