A method to Suppress Facial Expression in Posed and Spontaneous Videos
This addresses a domain-specific problem for face recognition applications by providing an incremental improvement in expression suppression.
The paper tackles the problem of facial expressions interfering with face recognition by introducing an optical strain suppression method that removes expressions from videos without needing expression-specific training. Experimental results on BU-4DFE and AM-FED datasets demonstrate the method's effectiveness in suppressing expressions like happiness, sadness, and anger.
We address the problem of suppressing facial expressions in videos because expressions can hinder the retrieval of important information in applications such as face recognition. To achieve this, we present an optical strain suppression method that removes any facial expression without requiring training for a specific expression. For each frame in a video, an optical strain map that provides the strain magnitude value at each pixel is generated; this strain map is then utilized to neutralize the expression by replacing pixels of high strain values with pixels from a reference face frame. Experimental results of testing the method on various expressions namely happiness, sadness, and anger for two publicly available data sets (i.e., BU-4DFE and AM-FED) show the ability of our method in suppressing facial expressions.