Intensity Estimation of Spontaneous Facial Action Units Based on Their Sparsity Properties
作者： Mohammad Reza Mohammadi , Emad Fatemizadeh , Mohammad H. Mahoor
DOI: 10.1109/TCYB.2015.2416317
关键词:
摘要: Automatic measurement of spontaneous facial action units (AUs) defined by the coding system (FACS) is a challenging problem. The recent FACS user manual defines 33 AUs to describe different activities and expressions. In expressions, subset are often occurred or activated at time. Given this fact that sparsely over time, we propose novel method detect absence presence estimate their intensity levels via sparse representation (SR). We use robust principal component analysis decompose expression from identity then multiple jointly using regression model formulated based on dictionary learning SR. Our experiments Denver UNBC-McMaster shoulder pain archive databases show our promising approach for AUs.
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