Predicting trait impressions of faces using local face recognition techniques


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.

Keywords Trait impressions; Face recognition; Face classification; Human–computer interaction; Overgeneralization effects; Classifier ensembles

[full paper]