An experimental comparison of gender classification methods
Pattern Recognition Letters
Fusing gait and face cues for human gender recognition
Neurocomputing
Gender classification based on feature selection using genetic algorithms
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Facial gender classification using shape-from-shading
Image and Vision Computing
Gender and ethnicity identification from silhouetted face profiles
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Extracting gender discriminating features from facial needle-maps
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Feature selection for efficient gender classification
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Supervised relevance maps for increasing the distinctiveness of facial images
Pattern Recognition
Bag of soft biometrics for person identification
Multimedia Tools and Applications
Integrating independent components and linear discriminant analysis for gender classification
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Gender discriminating models from facial surface normals
Pattern Recognition
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
Face description for perceptual user interfaces
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Investigating the impact of face categorization on recognition performance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Improved face recognition using extended modular principal component analysis
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Face recognition using optimized 3d information from stereo images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Design methodology for context-aware wearable sensor systems
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Semi-supervised feature selection for gender classification
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Human action recognition optimization based on evolutionary feature subset selection
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Three robust features extraction approaches for facial gender classification
The Visual Computer: International Journal of Computer Graphics
Gender classification of human face images based on adaptive features and support vector machines
Optical Memory and Neural Networks
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We consider the problem of gender classification from frontal facial images using genetic feature subsetselection. We argue that feature selection is an importantissue in gender classification and demonstrate that GeneticAlgorithms (GA) can select good subsets of features (i.e.,features that encode mostly gender information), reducingthe classification error. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector(i.e.,eigen-features) in a low-dimensional space. GeneticAlgorithms (GAs) are then employed to select a subset offeatures from the low-dimensional representation by disregarding certain eigenvectors that do not seem to encode important gender information. Four different classifiers werecompared in this study using genetic feature subset selection: a Bayes classifier, a Neural Network (NN) classifier,a Support Vector Machine (SVM) classifier, and a classifierbased on Linear Discriminant Analysis (LDA). Our experimental results show a significant error rate reduction in allcases. The best performance was obtained using the SVMclassifier. Using only 8.4%of the features in the completeset, the SVM classifier achieved an error rate of 4.7% froman average error rate of 8.9% using manually selected features.