Exploring Facial Asymmetry Using Optical Flow
- 11 April 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Signal Processing Letters
- Vol. 21 (7), 792-795
- https://doi.org/10.1109/lsp.2014.2316918
Abstract
Human faces are highly but not precisely bilaterally symmetrical. We present in this letter a measurement of facial asymmetry based on the optical flow field between a face image and its bilaterally mirrored counterpart. We revisit the problem of facial asymmetry quantification and confirm some conclusions in previous research by applying the proposed asymmetry measurement on a dataset containing 4000 subjects. Moreover, the proposed measurement also contains information on facial asymmetry compensation and thus can be used to facilitate various face processing tasks such as face image beautification, 3D face reconstruction and face recognition. Experimental results show the flexibility and effectiveness of our proposal.Funding Information
- National Natural Science Foundation of China (61101152)
This publication has 15 references indexed in Scilit:
- Continuous Pose Normalization for Pose-Robust Face RecognitionIEEE Signal Processing Letters, 2012
- Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
- Shadow compensation based on facial symmetry and image average for robust face recognitionNeurocomputing, 2010
- Human-assisted motion annotationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Robust Face Recognition via Sparse RepresentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
- Face recognition robust to left/right shadows; facial symmetryPattern Recognition, 2006
- Overview of the Face Recognition Grand ChallengePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- The CMU Pose, Illumination, and Expression (PIE) databasePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- From few to many: illumination cone models for face recognition under variable lighting and poseIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
- PCA versus LDAIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001