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• W2771369951 abstract "Humans are trying to interact with the computer via touch screen, smart-phones, audio and video. A computer get information from the human via an interface and likewise, a human recognize an information from the computer via an interface. Facial expression recognition is a key element in a human communication. In order to promote the man and machine interaction, a framework is proposed for the facial expression recognition from the images. The approach for representing the facial parts was done by Haar-Like features and the learning of these features was made possible by AdaBoost algorithm. Several application scenarios were proposed in terms of the feature extraction and emotion detection. As this system is capable of detecting the emotions from the images stored in a database, is going further here in this paper, an efficiency of emotion detection is giving the better response in terms of the time. The experimental results represents that, the proposed approach has the capability of detecting the false acceptance rate is 18% while the false rejection rate is just 5%." @default.
• W2771369951 created "2017-12-22" @default.
• W2771369951 creator A5065185939 @default.
• W2771369951 creator A5077947342 @default.
• W2771369951 date "2017-04-01" @default.
• W2771369951 modified "2023-09-26" @default.
• W2771369951 title "Perceive EEmotion from facial expressions" @default.
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• W2771369951 doi "https://doi.org/10.1109/iceca.2017.8212718" @default.
• W2771369951 hasPublicationYear "2017" @default.
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