Browsing by Author "Basavaraj, A."

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    Efficient Facial Expression Ecognition and classification system based on morphological processing of frontal face images
    (2016) Apte, A.; Basavaraj, A.; Nithin, R.K.
    Facial expressions are one of the many non-verbal cues that aid communication among humans. It has wide ranging applications from Human-computer interactions in computer vision to behavioral sciences and clinical practice in Psychology. Although, for humans recognizing facial expressions comes effortlessly, it is not so at the machine-level. To achieve, effective and efficient recognition of these varied expressions like in our brain, at machine-level, still remains a challenge. In this paper, morphological operations, statistical formulas and image processing techniques have been used to come up with a more efficient Facial Expression Recognition algorithm, using any frontal posed image. The entire process of facial expression recognition is divided into four categories, that is, Face detection, Facial feature localization using morphological operations, facial feature extraction using statistical formulas and finally, facial feature classification using neural networks. Facial expressions have been classified into six categories that are: joy, neutral, anger, sad, surprise and disgust. � 2015 IEEE.
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    Efficient Facial Expression Ecognition and classification system based on morphological processing of frontal face images
    (Institute of Electrical and Electronics Engineers Inc., 2016) Apte, A.; Basavaraj, A.; Nithin, R.K.
    Facial expressions are one of the many non-verbal cues that aid communication among humans. It has wide ranging applications from Human-computer interactions in computer vision to behavioral sciences and clinical practice in Psychology. Although, for humans recognizing facial expressions comes effortlessly, it is not so at the machine-level. To achieve, effective and efficient recognition of these varied expressions like in our brain, at machine-level, still remains a challenge. In this paper, morphological operations, statistical formulas and image processing techniques have been used to come up with a more efficient Facial Expression Recognition algorithm, using any frontal posed image. The entire process of facial expression recognition is divided into four categories, that is, Face detection, Facial feature localization using morphological operations, facial feature extraction using statistical formulas and finally, facial feature classification using neural networks. Facial expressions have been classified into six categories that are: joy, neutral, anger, sad, surprise and disgust. © 2015 IEEE.