论文标题
RarityNet：稀有指导性情感学习框架
RARITYNet: Rarity Guided Affective Emotion Learning Framework
论文作者
论文摘要
受到手工和深度学习方法的资产的启发，我们提出了RarityNet：稀有指导性情感学习框架，以学习外观特征并确定面部表情的情感类别。 RarityNet框架是通过结合浅层（稀有）和深（AffeMonet）功能来设计的，以识别来自挑战性图像的面部表情，例如自发表达，姿势变化，种族变化和照明条件。提出稀有性来编码当地社区中的室内过渡模式。 The AffEmoNet: affective emotion learning network is proposed by incorporating three feature streams: high boost edge filtering (HBSEF) stream, to extract the edge information of highly affected facial expressive regions, multi-scale sophisticated edge cumulative (MSSEC) stream is to learns the sophisticated edge information from multi-receptive fields and RARITY uplift complementary context feature (RUCCF) stream refines the RARITY-encoded功能并帮助MSSEC流功能丰富RarityNet的学习能力。
Inspired from the assets of handcrafted and deep learning approaches, we proposed a RARITYNet: RARITY guided affective emotion learning framework to learn the appearance features and identify the emotion class of facial expressions. The RARITYNet framework is designed by combining the shallow (RARITY) and deep (AffEmoNet) features to recognize the facial expressions from challenging images as spontaneous expressions, pose variations, ethnicity changes, and illumination conditions. The RARITY is proposed to encode the inter-radial transitional patterns in the local neighbourhood. The AffEmoNet: affective emotion learning network is proposed by incorporating three feature streams: high boost edge filtering (HBSEF) stream, to extract the edge information of highly affected facial expressive regions, multi-scale sophisticated edge cumulative (MSSEC) stream is to learns the sophisticated edge information from multi-receptive fields and RARITY uplift complementary context feature (RUCCF) stream refines the RARITY-encoded features and aid the MSSEC stream features to enrich the learning ability of RARITYNet.
