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In order to understand how real users experience a website or application, researchers can use facial expression technology to analyze participants’ facial expressions during testing. Happy) may predict an advertisement’s success or a positive shift in attitude towards the brand.
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Emotional responses to advertisements tend to have a heavy influence on consumers’ intentions to buy a product – using facial coding analysis, researchers could perform testing and present evidence that a certain expression (ex. Some notable examples of fields using facial expression research include:įaceReader can be used to examine metrics such as consumer interest in a product and the effectiveness of video advertisements. Fields of research using facial expression detectionīecause FaceReader can serve as an automated, non-intrusive measure of engagement, it can be used in many applications. FaceReader’s Action Unit Module is capable of analyzing 20 of these action units, including Cheek Raiser, Nose Wrinkler, and Dimpler.Īction Unites are responsible for facial expressions. The movements of individual facial muscles are broken down into specific action units. To backtrack a little, the FACS is a facial expression coding system meant for measuring facial expressions and describing all observable facial movement. Action unit analysisĪutomated facial coding can be further extended to an action unit level, according to the FACS (Facial Action Coding System). In addition, FaceReader can also calculate gaze direction, head orientation, and characteristics such as gender, and age. Once classified, emotions can be represented as line and/or bar graphs as well as in a pie chart, which shows the percentage per emotion. Types of input sources commonly used with FaceReader include video analysis, live analysis using a webcam, and still images.
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This allows the software to analyze the face even if a part of it is hidden Deep face classification – This method allows FaceReader to directly classify the face from image pixels using an artificial neural network to recognize patterns.
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