A receptive field based approach for face detection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Facial gender classification using shape-from-shading
Image and Vision Computing
Gender recognition: A multiscale decision fusion approach
Pattern Recognition Letters
Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques
International Journal of Computer Vision
Multi-view gender classification using hierarchical classifiers structure
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Gender classification by principal component analysis and support vector machine
Proceedings of the 2011 International Conference on Communication, Computing & Security
Gender classification using the profile
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Combining contrast information and local binary patterns for gender classification
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Age regression from soft aligned face images using low computational resources
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Can gender be predicted from near-infrared face images?
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Gender classification via global-local features fusion
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Learning local binary patterns for gender classification on real-world face images
Pattern Recognition Letters
Gender recognition via locality preserving tensor analysis on face images
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
On the importance of multi-dimensional information in gender estimation from face images
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Gender identification using feature patch-based bayesian classifier
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Soft biometric classification using local appearance periocular region features
Pattern Recognition
Local gradient increasing pattern (LGIP) for facial representation and gender recognition
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Gender classification from unaligned facial images using support subspaces
Information Sciences: an International Journal
Understanding critical factors in appearance-based gender categorization
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Gender recognition using cognitive modeling
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Improving gender recognition using genetic algorithms
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Measuring the degree of face familiarity based on extended NMF
ACM Transactions on Applied Perception (TAP)
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Robust gender recognition by exploiting facial attributes dependencies
Pattern Recognition Letters
Three robust features extraction approaches for facial gender classification
The Visual Computer: International Journal of Computer Graphics
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We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the best possible classification accuracy.