The nature of statistical learning theory
The nature of statistical learning theory
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Boosting Sex Identification Performance
International Journal of Computer Vision
An Experimental Study on Automatic Face Gender Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Gender Classification on Consumer Images in a Multiethnic Environment
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Hierarchical ensemble of global and local classifiers for face recognition
IEEE Transactions on Image Processing
Generalized multi-ethnic face age-estimation
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Gender Classification Using Local Directional Pattern (LDP)
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Revisiting Linear Discriminant Techniques in Gender Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble of local and global information for finger-knuckle-print recognition
Pattern Recognition
Multi-view gender classification using local binary patterns and support vector machines
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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Computer vision based gender classification is an interesting and challenging research topic in visual surveillance and human-computer interaction systems. In this paper, based on the results of psychophysics and neurophysiology studies that both local and global information is crucial for the image perception, we present an effective global-local features fusion (GLFF) method for gender classification. First, the global features are extracted based on active appearance models (AAM) and the local features are extracted by LBP operator. Second, the global features and local features are fused by sequent selection for gender classification. Third, gender is predicted based on the selected features via support vector machines (SVM). The experimental results show that the proposed local-global information combination scheme could significantly improve the gender classification accuracy obtained by either local or global features, leading to promising performance.