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Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions plus neutral at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression.
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Facial Emotion Recognition Based on Cascade of Neural Networks
Facial Emotion Recognition using Convolutional Neural Networks - IEEE Conference Publication
The chapter presents a method that uses the cascade of neural networks for facial expression recognition. As an input the algorithm receives a normalized image of a face and returns the emotion that the face expresses. To determine the best classifiers for recognizing particular emotions one- and multilayered networks were tested. Experiments covered different resolutions of the images presenting faces as well as the images including regions of mouths and eyes. On the basis of the tests results a cascade of the neural networks was proposed. The cascade recognizes six basic emotions and neutral expression. Unable to display preview.
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Facial expression recognition is the task of classifying the expressions on face images into various categories such as anger, fear, surprise, sadness, happiness and so on. Image credit: DeXpression. Browse State-of-the-Art.
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