Abstract
The aim in this paper is to show how to use the 2.5D facial surface normals (needle-maps) recovered using shape from shading (SFS) to improve the performance of gender classification. We incorporate principal geodesic analysis (PGA) into SFS to guarantee the recovered needle-maps is a possible example defined by a statistical model. Because the recovered facial needlemaps satisfy data-closeness constraint, they not only give the facial shape information, but also combine the image intensity implicitly. Experiments show that this combination gives better gender classification performance than using facial shape or texture information alone.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
