FacePoseNet: Making a Case for Landmark-Free Face Alignment

Feng Ju Chang, Anh Tuan Tran, Tal Hassner, Iacopo Masi, Ram Nevatia, Gerard Medioni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We show how a simple convolutional neural network (CNN) can be trained to accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose, directly from image intensities. We further explain how this FacePoseNet (FPN) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks. We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments. Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method. Our results show that (a) better landmark detection accuracy measured on the 300W benchmark does not necessarily imply better face recognition accuracy. (b) Our FPN provides superior 2D and 3D face alignment on both benchmarks. Finally, (c), FPN aligns faces at a small fraction of the computational cost of comparably accurate landmark detectors. For many purposes, FPN is thus a far faster and far more accurate face alignment method than using facial landmark detectors.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1599-1608
Number of pages10
ISBN (Electronic)9781538610343
DOIs
StatePublished - 1 Jul 2017
Event16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Volume2018-January

Conference

Conference16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Country/TerritoryItaly
CityVenice
Period22/10/1729/10/17

Bibliographical note

Funding Information:
This research is based upon work supported in part by theOffice oftheDirectorofNationalIntelligence(ODNI), IntelligenceAdvanced ResearchProjectsActivity (IARPA), viaIARPA2014-14071600011. Theviews andconclusions containedhereinarethoseoftheauthorsandshouldnot be interpretedas necessarilyrepresenting theofficial poli-ciesorendorsements, eitherexpressedorimplied, ofODNI, IARPA, ortheU.S.Government. TheU.S.Governmentis authorizedtoreproduceanddistribute reprintsforGovern-mental purpose notwithstanding any copyright annotation thereon.

Funding Information:
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA 2014-14071600011. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.

Fingerprint

Dive into the research topics of 'FacePoseNet: Making a Case for Landmark-Free Face Alignment'. Together they form a unique fingerprint.

Cite this