Differential components of discriminative 2D Gaussian–Hermite moments for recognition of facial expressions
作者：
Highlights：
• A novel facial expression recognition algorithm using 2D Gaussian–Hermite moments.
• New approach of discriminative selection of moments as features of expression.
• New subspace to estimate differential components of moments as expressive features.
• Experiments on challenging datasets having posed, spontaneous, and wild expressions.
• Results show that proposed method is better than existing or similar methods.
摘要
Highlights•A novel facial expression recognition algorithm using 2D Gaussian–Hermite moments.•New approach of discriminative selection of moments as features of expression.•New subspace to estimate differential components of moments as expressive features.•Experiments on challenging datasets having posed, spontaneous, and wild expressions.•Results show that proposed method is better than existing or similar methods.
论文关键词：Differentially expressive components,Discriminative moments,Expression classification,Expressions in-the-wild,Gaussian–Hermite moments,Posed and spontaneous expressions
论文评审过程：Received 5 July 2015, Revised 1 February 2016, Accepted 3 March 2016, Available online 14 March 2016, Version of Record 12 April 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.03.006
