Applying FaceReader to Recognize Consumer Emotions in Graphic Styles

Yu, Chia-yin; Ko, Cheng−Hao

doi:10.1016/j.procir.2017.01.014

Cited by 39 publications

(24 citation statements)

References 20 publications

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“…Significant trends in happiness, surprise, and disgust were observed across the experimental images. Researchers noted that while simple elements and sharp edges showed higher attention and valence, higher valence was observed for pictorial images as opposed to computer-generated images [64]. A study on chocolate packaging did not find any significant differences in AFEA data for the experimental package images [65].…”

Section: Differentiation Of Stimuli By Expressed Emotionmentioning

confidence: 96%

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The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

2020

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In the late 1970s, analysis of facial expressions to unveil emotional states began to grow and flourish along with new technologies and software advances. Researchers have always been able to document what consumers do, but understanding how consumers feel at a specific moment in time is an important part of the product development puzzle. Because of this, biometric testing methods have been used in numerous studies, as researchers have worked to develop a more comprehensive understanding of consumers. Despite the many articles on automated facial expression analysis (AFEA), literature is limited in regard to food and beverage studies. There are no standards to guide researchers in setting up materials, processing data, or conducting a study, and there are few, if any, compilations of the studies that have been performed to determine whether any methodologies work better than others or what trends have been found. Through a systematic Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review, 38 articles were found that were relevant to the research goals. The authors identified AFEA study methods that have worked and those that have not been as successful and noted any trends of particular importance. Key takeaways include a listing of commercial AFEA software, experimental methods used within the PRISMA analysis, and a comprehensive explanation of the critical methods and practices of the studies analyzed. Key information was analyzed and compared to determine effects on the study outcomes. Through analyzing the various studies, suggestions and guidance for conducting and analyzing data from AFEA experiments are discussed.

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Section: Differentiation Of Stimuli By Expressed Emotionmentioning

confidence: 96%

“…One study used the reduction in negative responses as an indicator of positive preference [63]. Studies on odors and images have shown that arousal/attention may be more important than valence for the characterization of samples by AFEA [51,64].…”

Section: Differentiation Of Stimuli By Expressed Emotionmentioning

confidence: 99%

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

2020

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“…Originally built upon another software called CERT (Littlewort et al, 2011), FACET was distributed by Emotient, whereas FaceReader was developed and first presented by VicarVision in 2005 (Den Uyl andVan Kuilenburg, 2005). Both systems have been used in a large number of scientific studies (e.g., Skiendziel, Rösch, & Schultheiss, 2019; for a review see Lewinski et al, 2014a) as well as in consumer behavior (Garcia-Burgos and Zamora, 2013;Danner et al, 2014;Yu and Ko, 2017) and marketing research (Lewinski et al, 2014b;McDuff et al, 2014). Nonetheless, there are several other promising off-the-shelf classifiers available today that could be employed for the same purposes.…”

Section: Abundant Choices: Classifiers Lack Cross-system Validationmentioning

confidence: 99%

Opportunities and challenges for using automatic human affect analysis in consumer research

Küster¹,

Krumhuber²,

Steinert³

et al. 2020

Preprint

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The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as promising input for two-step machine learning in combination with other (multimodal) features. We illustrate this point in a case study using facial activity as input features to predict crying behavior in response to sad movies. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research.

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“…This method is to minimize the number of neurons with a probability of 0.2. Next is to calculate the input gate wherein it will decide which value will be updated, as shown in (6)…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Previous studies measured positive emotions and negative emotions that impact on loyalty intentions. Positive emotions are happy, happy, and enthusiastic, while those that include negative emotions are angry, disappointed, and hesitant [5] The introduction of sensations is also used to find out the customer's emotional reactions to different types of images [6].…”

Section: Introductionmentioning

confidence: 99%

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

Rasyid¹,

Djamal²

2019

EECSI

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One method concerning evaluating video ads is neuromarketing. This information comes from the viewer's mind, thus minimizing subjectivity. Besides, neuromarketing can overcome the difficulties of respondents who sometimes do not know the response to the video ads they watch. Neuromarketing involves information from what customers think is captured by an Electroencephalogram (EEG) device. Meanwhile, if the electrical signal information in the brain is corresponding to an understanding of one's psychology, it is called neuropsychology. This field consists of emotions, attention, and concentration variables. This research offered neuromarketing base on emotion and attention of EEG signal using Wavelet extraction and Recurrent Neural Networks. Measurement of EEG variable every two seconds while watching video ads. The results showed that Wavelet and Recurrent Neural Networks could provide training data accuracy of 100% and 89.73% for new data. The experiment also gave that the RMSprop optimization model for the weight correction contributed to higher correctness of 1.34% than the Adam model. Meanwhile, using Wavelet for extraction can increase accuracy by 5%.

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Applying FaceReader to Recognize Consumer Emotions in Graphic Styles

Cited by 39 publications

References 20 publications

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

Opportunities and challenges for using automatic human affect analysis in consumer research

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

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