1 / 2

Session 3D: Thursday Afternoon, June 27th

Session 3D: Thursday Afternoon, June 27th. Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression Recognition Ziheng Wang , Shangfei Wang, Qiang Ji. P3D-06. Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression Recognition.

misae
Télécharger la présentation

Session 3D: Thursday Afternoon, June 27th

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Session 3D: Thursday Afternoon, June 27th Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression Recognition Ziheng Wang, Shangfei Wang, Qiang Ji

  2. P3D-06 Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression Recognition • Introduction • Model the facial expression as a complex activity consisting of sequential or overlapping facial muscle events • Propose an Interval Temporal Bayesian Network (ITBN) to capture spatio-temporal relations among the primitive facial events for expression recognition Interval Temporal Bayesian Network B before meet E1 A IAB overlap start IBC C B E2 IAC C during B C E3 finish equal B C E4 B C • ExperimentalResults • ITBN outperforms other time-slice based dynamic models such as HMM • ITBN achieves comparable and even better performance than the related works B t C Contributions C B B C

More Related