Abstract
In this paper, we investigate gender classification based on 2.5D facial surface normals (facial needle-maps) which can be recovered from 2D intensity images using a non-lambertian Shape-from-shading (SFS) method. We also describe a weighted principal geodesic analysis (WPGA) method to extract features from facial surface normals. By incorporating the weight matrix into principal geodesic analysis (PGA), we control the obtained principal variance axes to be in the direction of the variance on gender information. For classification, an a posteriori probability based method is adopted. Experimental results confirms that using WPGA increases the gender discriminating power in the leading eigenvectors, and also demonstrates the feasibility of gender classification based on facial shape information.
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.
