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Effective Emotion Recognition from Partially Occluded Facial Images Using Deep Learning

机译:利用深层学习的部分封闭面部图像有效的情感识别

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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.
机译:有效的表达分析取决于面部特征的准确表示。 无论姿势,面部形状,照明和图像分辨率都是非常重要的,对不同的面部肌肉进行适当的识别和跟踪,对服务目的非常重要。 但是,基于面部和外观的特征的提取和分析失败,面部对准和闭塞不当。 这些问题的少数现有的作品主要决定了基于培训数据歧视表达的面部区域。 然而,在这些方法中,面部贴片的位置和尺寸根据训练数据而变化,其固有地使得难以构想通用系统以满足该目的。 本文提出了一种新的面部地标检测技术,以及基于ACNN的突出贴片的面部表情识别框架,具有不同图像分辨率的显着性能。

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