Intensity Rank Estimation of Facial Expressions Based on a Single Image
Kuang-Yu Chang, Chu‐Song Chen, Yi‐Ping Hung · 2013
In this paper, we propose a framework that estimates the discrete intensity rank of a facial expression based on a single image. For most people, judging whether an expression is more intense than others is easier than determining its real-valued intensity degree, and hence the relative order of two expressions is more distinguishable than the exact difference between them. We utilize the relative order to construct an image-based ranking approach for inferring the discrete ranks. The challenge of image-based approaches is to conduct a representation for subtle expression changes. We employ an efficient descriptor, scattering transform, which is translation invariant and can linearize deformations. This scattering representation recovers the lost high frequencies and retains discrimination under invariant property. Our experimental results demonstrate that the proposed framework with scattering transform outperforms other compared feature descriptors and algorithms.