Expression facial research

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Look at the picture above. Do you think the young woman is surprised? You may be wrong.

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It automatically detects a number of specific properties in facial images, such as facial expressions. FaceReader is used in nearly 1, publications! FaceReader has been trained to classify the expressions happy, sad, angry, surprised, scared, disgusted, and neutral.

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The field of facial expression analysis is over a hundred years old, and has now come of age. The detection of expressions and emotions by automatic analysis has matured into a reliable methodology that is widely used in a variety of research. Ekman — essentially the father of facial expression analysis — is inevitably the prime feature of this list.

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With the emerging advanced technologies in hardware and sensors, FER systems have been developed to support real-world application scenes, instead of laboratory environments. We focus on those sensors that may provide extra information and help the FER systems to detect emotion in both static images and video sequences. The first group is detailed-face sensors, which detect a small dynamic change of a face component, such as eye-trackers, which may help differentiate the background noise and the feature of faces. The second is non-visual sensors, such as audio, depth, and EEG sensors, which provide extra information in addition to visual dimension and improve the recognition reliability for example in illumination variation and position shift situation.

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While conducting research on emotions and facial expressions in Papua New Guinea inpsychologist Carlos Crivelli discovered something startling. He showed Trobriand Islanders photographs of the standard Western face of fear — wide-eyed, mouth agape — and asked them to identify what they saw. Instead, they saw an indication of threat and aggression.

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The purpose of this research study is to learn more about how children and adults process emotion in the faces that they see around them. We are particularly interested in how much information children need, as compared to adults, in order to recognize emotion in a face and the brain activity that is associated with recognizing these emotions. We also are interested in whether looking at different parts of the face has an effect on whether or not children and adults recognize emotions in faces.

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However, it is unclear whether social context plays a moderating role in forming trustworthiness judgments. Based on the emotions as social information EASI model, differing contexts may impact the effect of facial expression; however, there is no evidence demonstrating that differing contexts will or will not influence the effect of facial gender. In this study, we used two experiments to examine how facial expression and facial gender affect facial trustworthiness judgments and whether the effects on facial trustworthiness judgments are consistent in cooperative and competitive settings.

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Figure 1. Sample images from the database. Top row left to right : character 'aia' with expression 'fear', character 'bonnie' with expression 'joy' and character 'jules' with expression 'disgust'.

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FaceReader is the most robust automated system for the recognition of a number of specific properties in facial images, including the six basic or universal expressions: happy, sad, angry, surprised, scared, and disgusted. Additionally, FaceReader can recognize a 'neutral' state and analyze 'contempt'. FaceReader software is fast, flexible, accurate, and easy to use.

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