Skip to content

Realistic facial expression reconstruction for VR HMD users

Research output: Contribution to journalArticle

We present a system for sensing and reconstructing facial expressions of the virtual reality (VR) head-mounted display (HMD) user. The HMD occludes a large portion of the user’s face, which makes most existing facial performance capturing techniques intractable. To tackle this problem, a novel hardware solution with electromyography (EMG) sensors being attached to the headset frame is applied to track facial muscle movements. For realistic facial expression recovery, we first reconstruct the user’s 3D face from a single image and generate the personalized blendshapes associated with seven facial action units (AUs) on the most emotionally salient facial parts (ESFPs). We then utilize preprocessed EMG signals for measuring activations of AU-coded facial expressions to drive pre-built personalized blendshapes. Since facial expressions appear as important nonverbal cues of the subject’s internal emotional states, we further investigate the relationship between six basic emotions - anger, disgust, fear, happiness, sadness and surprise, and detected AUs using a fern classifier. Experiments show the proposed system can accurately sense and reconstruct high-fidelity common facial expressions while providing useful information regarding the emotional state of the HMD user.
Original languageEnglish
JournalIEEE Transactions on Multimedia
Publication statusAccepted for publication - 30 Jul 2019

Documents

  • Realistic facial expression reconstruction

    Rights statement: The embargo end date of 2050 is a temporary measure until we know the publication date. Once we know the publication date the full text of this article will be able to view shortly afterwards.

    Accepted author manuscript (Post-print), 6.62 MB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 1/01/50

Related information

Relations Get citation (various referencing formats)

ID: 15355919