Application of Automated Facial Expression Analysis and Facial Action Coding System to Assess Affective Response to Consumer Products

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Virginia Tech


Sensory and consumer sciences seek to comprehend the influences of sensory perception on consumer behaviors such as product liking and purchase. The food industry assesses product liking through hedonic testing but often does not capture affectual response as it pertains to product-generated (PG) and product-associated (PA) emotions. This research sought to assess the application of PA and PG emotion methodology to better understand consumer experiences. A systematic review of the existing literature was performed that focused on the Facial Action Coding System (FACS) and its use to investigate consumer affect and characterize human emotional response to product-based stimuli, which revealed inconsistencies in how FACS is carried out as well as how emotional response is inferred from Action Unit (AU) activation. Automatic Facial Expression Analysis (AFEA), which automates FACS and translates the facial muscular positioning into the basic universal emotions, was then used in a two-part study. In the first study (n=50 participants), AFEA, a Check-All-That-Apply (CATA) emotions questionnaire, and a Single-Target Implicit Association Test (ST-IAT) were used to characterize the relationship between PA as well as PG emotions and consumer behavior (acceptability, purchase intent) towards milk in various types of packaging (k=6). The ST-IAT did not yield significant PA emotions for packaged milk (p>0.05), but correspondence analysis of CATA data produced PA emotion insights including term selection based on arousal and underlying approach/withdrawal motivation related to packaging pigmentation. Time series statistical analysis of AFEA data provided increased insights on significant emotion expression, but the lack of difference (p>0.05) between certain expressed emotions that maintain no related AUs, such as happy and disgust, indicates that AFEA software may not be identifying AUs and determining emotion-based inferences in agreement with FACS. In the second study, AFEA data from the sensory evaluation (n=48 participants) of light-exposed milk stimuli (k=4) stored in packaging with various light-blocking properties) underwent time series statistical analysis to determine if the sensory-engaging nature of control stimuli could impact time series statistical analysis of AFEA data. When compared against the limited sensory engaging (blank screen) control, contempt, happy, and angry were expressed more intensely (p<0.025) and with greater incidence for the light-exposed milk stimuli; neutral was expressed exclusively in the same manner for the blank screen. Comparatively, intense neutral expression (p<0.025) was brief, fragmented, and often accompanied by intense (albeit fleeting) expressions of happy, sad, or contempt for the sensory engaging control (water); emotions such as surprised, scared, and sad were expressed similarly for the light-exposed milk stimuli. As such, it was determined that care should be taken while comparing the control and experimental stimuli in time series analysis as facial activation of muscles/AUs related to sensory perception (e.g., chewing, smelling) can impact the resulting interpretation. Collectively, the use of PA and PG emotion methodology provided additional insights on consumer-product related behaviors. However, it is hard to conclude whether AFEA is yielding emotional interpretations based on true facial expression of emotion or facial actions related to sensory perception for consumer products such as foods and beverages.



automated facial expression analysis, facial action coding system, milk, packaging, sensory science, emotion