The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling physical personalities for virtual agents by modeling trait impressions of the face: a neural network analysis
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Weighted Sub-Gabor for face recognition
Pattern Recognition Letters
A hybrid wavelet-based fingerprint matcher
Pattern Recognition
Switching class labels to generate classification ensembles
Pattern Recognition
AUC: a better measure than accuracy in comparing learning algorithms
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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The aim of this work is to propose a method for detecting the social meanings that people perceive in facial morphology using local face recognition techniques. Developing a reliable method to model people's trait impressions of faces has theoretical value in psychology and human-computer interaction. The first step in creating our system was to develop a solid ground truth. For this purpose, we collected a set of faces that exhibit strong human consensus within the bipolar extremes of the following six trait categories: intelligence, maturity, warmth, sociality, dominance, and trustworthiness. In the studies reported in this paper, we compare the performance of global face recognition techniques with local methods applying different classification systems. We find that the best performance is obtained using local techniques, where support vector machines or Levenberg-Marquardt neural networks are used as stand-alone classifiers. System performance in each trait dimension is compared using the area under the ROC curve. Our results show that not only are our proposed learning methods capable of predicting the social impressions elicited by facial morphology but they are also in some cases able to outperform individual human performances.