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Effective Emotion Recognition from Partially Occluded Facial Images Using Deep Learning

Abstract : Effective expression analysis hugely depends upon the accurate representation of facial features. Proper identification and tracking of different facial muscles irrespective of pose, face shape, illumination, and image resolution is very much essential for serving the purpose. However, extraction and analysis of facial and appearance based features fails with improper face alignment and occlusions. Few existing works on these problems mainly determine the facial regions which contribute towards discrimination of expressions based on the training data. However, in these approaches, the positions and sizes of the facial patches vary according to the training data which inherently makes it difficult to conceive a generic system to serve the purpose. This paper proposes a novel facial landmark detection technique as well as a salient patch based facial expression recognition framework based on ACNN with significant performance at different image resolutions.
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Submitted on : Thursday, November 18, 2021 - 2:21:14 PM
Last modification on : Thursday, November 18, 2021 - 2:31:56 PM
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Smitha Engoor, Sendhilkumar Selvaraju, Hepsibah Sharon Christopher, Mahalakshmi Guruvayur Suryanarayanan, Bhuvaneshwari Ranganathan. Effective Emotion Recognition from Partially Occluded Facial Images Using Deep Learning. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.213-221, ⟨10.1007/978-3-030-63467-4_17⟩. ⟨hal-03434797⟩



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