A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Recovering Facial Shape and Albedo Using a Statistical Model of Surface Normal Direction
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multimodal facial gender and ethnicity identification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Classification of face images for gender, age, facial expression, and identity
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Recognising Facial Expressions Using Spherical Harmonics
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Supervised Principal Geodesic Analysis on Facial Surface Normals for Gender Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Reconstructing 3D Facial Shape Using Spherical Harmonics
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Facial gender classification using shape-from-shading
Image and Vision Computing
Extracting gender discriminating features from facial needle-maps
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Supervised relevance maps for increasing the distinctiveness of facial images
Pattern Recognition
Face recognition using simplicial complexes
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Gender discriminating models from facial surface normals
Pattern Recognition
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In this paper, we describe a weighted principal geodesic analysis (WPGA) method to extract features for gender classification based on 2.5D facial surface normals (needle-maps) which can be extracted from 2D intensity images using shape-from-shading (SFS). By incorporating the weight matrix into principal geodesic analysis (PGA), we control the obtained principal axis to be in the direction of the variance on gender information. Experiments show that using WPGA, the leading eigenvectors encode more gender discriminating power than using PGA, and that gender classification based on leading WPGA parameters is more accurate and stable than based on leading PGA parameters.