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Expression robust 3D face landmarking using thresholded surface normals
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
Univ Bath, Dept Elect & Elect Engn, Bath, Avon, England.
2018 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 78, p. 120-132Article in journal (Refereed) Published
Abstract [en]

3D face recognition is an increasing popular modality for biometric authentication, for example in the iPhoneX. Landmarking plays a significant role in region based face recognition algorithms. The accuracy and consistency of the landmarking will directly determine the effectiveness of feature extraction and hence the overall recognition performance. While surface normals have been shown to provide high performing features for face recognition, their use in landmarking has not been widely explored. To this end, a new 3D facial landmarking algorithm based on thresholded surface normal maps is proposed, which is applicable to widely used 3D face databases. The benefits of employing surface normals are demonstrated for both facial roll and yaw rotation calibration and nasal landmarks localization. Results on the Bosphorus, FRGC and BU-3DFE databases show that the detected landmarks possess high within class consistency and accuracy under different expressions. For several key landmarks the performance achieved surpasses that of state-of-the-art techniques and is also training free and computationally efficient. The use of surface normals therefore provides a useful representation of the 3D surface and the proposed landmarking algorithm provides an effective approach to localising the key nasal landmarks.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2018. Vol. 78, p. 120-132
Keywords [en]
3D face landmarking, Surface normals
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-353096DOI: 10.1016/j.patcog.2018.01.011ISI: 000428490900009OAI: oai:DiVA.org:uu-353096DiVA, id: diva2:1221581
Available from: 2018-06-20 Created: 2018-06-20 Last updated: 2018-06-20Bibliographically approved

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