EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
SexNet: A neural network identifies sex from human faces
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
The computational brain
The nature of statistical learning theory
The nature of statistical learning theory
A perceptron reveals the face of sex
Neural Computation
Journal of Mathematical Psychology
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and recognition in vision
Representation and recognition in vision
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Gender Classification of Human Faces
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Face Recognition Algorithms as Models of Human Face Processing
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Representation, Similarity, and the Chorus of Prototypes
Representation, Similarity, and the Chorus of Prototypes
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
EMPATH: A Neural Network that Categorizes Facial Expressions
Journal of Cognitive Neuroscience
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Classification in a normalized feature space using support vector machines
IEEE Transactions on Neural Networks
On the Dimensionality of Face Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
The Feature Importance Ranking Measure
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Prognose coronary heart diseases through sphygmogram analysis and SVM classifier
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Behavior-constrained support vector machines for fMRI data analysis
IEEE Transactions on Neural Networks
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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We attempt to shed light on the algorithms humans use to classify images of human faces according to their gender. For this, a novel methodology combining human psychophysics and machine learning is introduced. We proceed as follows. First, we apply principal component analysis (PCA) on the pixel information of the face stimuli. We then obtain a data set composed of these PCA eigenvectors combined with the subjects' gender estimates of the corresponding stimuli. Second, we model the gender classification process on this data set using a separating hyperplane (SH) between both classes. This SH is computed using algorithms from machine learning: the support vector machine (SVM), the relevance vector machine, the prototype classifier, and the K-means classifier. The classification behavior of humans and machines is then analyzed in three steps. First, the classification errors of humans and machines are compared for the various classifiers, and we also assess how well machines can recreate the subjects' internal decision boundary by studying the training errors of the machines. Second, we study the correlations between the rank-order of the subjects' responses to each stimulus—the gender estimate with its reaction time and confidence rating—and the rank-order of the distance of these stimuli to the SH. Finally, we attempt to compare the metric of the representations used by humans and machines for classification by relating the subjects' gender estimate of each stimulus and the distance of this stimulus to the SH. While we show that the classification error alone is not a sufficient selection criterion between the different algorithms humans might use to classify face stimuli, the distance of these stimuli to the SH is shown to capture essentials of the internal decision space of humans. Furthermore, algorithms such as the prototype classifier using stimuli in the center of the classes are shown to be less adapted to model human classification behavior than algorithms such as the SVM based on stimuli close to the boundary between the classes.