Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Sparse models for gender classification
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Mixture of experts for classification of gender, ethnic origin, and pose of human faces
IEEE Transactions on Neural Networks
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
A Framework for Multi-view Gender Classification
Neural Information Processing
Gaussian Processes for Object Categorization
International Journal of Computer Vision
Facial gender classification using shape-from-shading
Image and Vision Computing
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Gender discriminating models from facial surface normals
Pattern Recognition
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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This paper concerns the gender classification task of discriminating between images of faces of men and women from face images. In appearance-based approaches, the initial images are preprocessed (e.g. normalized) and input into classifiers. Recently, support vector machines (SVMs) which are popular kernel classifiers have been applied to gender classification and have shown excellent performance. SVMs have difficulty in determining the hyperparameters in kernels (using cross-validation). We propose to use Gaussian process classifiers (GPCs) which are Bayesian kernel classifiers. The main advantage of GPCs over SVMs is that they determine the hyperparameters of the kernel based on Bayesian model selection criterion. The experimental results show that our methods outperformed SVMs with cross-validation in most of data sets. Moreover, the kernel hyperparameters found by GPCs using Bayesian methods can be used to improve SVM performance.