â¦How to use video camera recording to predict face skin health product consumer's emotion .How to use automated facial expression analysis for emotion and behavior prediction.
The expression of emotion is achieved through combinations of verbal and nonverbal information produced from various sources of the body and the brains.
Nonverbal information encompasses any message that is not expressed in words, including gestures, postures to be performed from individual behavior for individual daily life.
Though people often don't invest much thought to the nonverbal aspect of communication to be performed to manage emotional experiences.
Computers enable researchers to process to gather data in a short amount of time to predict facial expression consumers.
The new methodological for social scientists may be a valid analysis to automated facial expression from consumer's daily behavior at home experiment for several days investigation more absolutely.
Among the various models of nonverbal communication, we focus on facial expression which are captured by small digital cameras and later analyzed with computer software.
As Webb et. al. (2000) pointed out, ''people are low-fidelity observational instruments recording and interpretation may be erratic over time, as the observer learns and responds to the research phenomena he or she observes.
It means that we can observe consumer behavior to predict why who buy the product from whose daily life behavior performance.
Recent studies applied automated feature extraction and classification to extract macro features .
Such as the head and hand position and angle from video features taken during an experiment where a theft took place.
It also implied that computer models obtained up to 71 percent correct classification of innocent or guilty participants based on the macro features extracted from the video camera.
Furthermore, in an overview of detection research." Meservy et al.(2008) noted that ''the accuracy of humans coding behavioral indicators only falls around 50 percent, but that computers trained to a automatically extract and identify relevant behavioral cues detect deception with significantly higher accuracy.
Furthermore, computers operate without the other methods( e.g. physiological measures such as polygraph machines or lie detectors) and the lost of extensively trained human interviewers.'' Ambady and Rosenthal (1992) showed that ''another advantage of why automated facial detection technology coupled with computational models is that once the system secures the parameters for a model, prediction of behavior ( vs simple detection and classification) can be made using only a small sample.
This is a computational recording of what social psychologists, a way people sample a short except from social behavior to draw inferences of about states, traits and other personally relevant characteristics .For instance, based on an observation of a three minute video clip of a conflict between a married couple.'' Carrere and Gottman(1999) also indicated ''video cameras were able to predict the outcome of that marriage after six years.
Using machine learning coupled with computer vision allows computers to cause this human cognitive process; models are trained on a short sample of facial features and those features automatically predict future behaviors.
Computers were used in place of human coders to detect vocal behaviors ( e.g. time spent speaking, influence over conversation partners, variation in pitch and volume and behavior mirroring) during a negotiation task.
