Automatic expression spotting in videos |
| |
Authors: | Matthew Shreve Jesse BrizziSergiy Fefilatyev Timur LuguevDmitry Goldgof Sudeep Sarkar |
| |
Affiliation: | Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA |
| |
Abstract: | In this paper, we propose a novel solution for the problem of segmenting macro- and micro-expression frames (or retrieving the expression intervals) in video sequences, which is a prior step for many expression recognition algorithms. The proposed method exploits the non-rigid facial motion that occurs during facial expressions by capturing the optical strain corresponding to the elastic deformation of facial skin tissue. The method is capable of spotting both macro-expressions which are typically associated with expressed emotions and rapid micro- expressions which are typically associated with semi-suppressed macro-expressions. We test our algorithm on several datasets, including a newly released hour-long video with two subjects recorded in a natural setting that includes spontaneous facial expressions. We also report results on a dataset that contains 75 feigned macro-expressions and 37 feigned micro-expressions. We achieve over a 75% true positive rate with a 1% false positive rate for macro-expressions, and a nearly 80% true positive rate for spotting micro-expressions with a .3% false positive rate. |
| |
Keywords: | Expression spotting Macro-expressions Micro-expressions |
本文献已被 ScienceDirect 等数据库收录! |
|