A Benchmark for Geometric Facial Beauty Study

2nd International Conference on Medical Biometrics, ICMB 2010, Hong Kong, 28-30 June 2010This paper presents statistical analyses for facial beauty study. A large-scale database was built, containing 23412 frontal face images, 875 of them are marked as beautiful. We focus on the geometric feature defined by a set of landmarks on faces. A normalization approach is proposed to filter out the non-shape variations - translation, rotation, and scale. The normalized features are then mapped to its tangent space, in which we conduct statistical analyses: Hotelling's T2 test is applied for testing whether female and male mean faces have significant difference; Principal Component Analysis (PCA) is applied to summarize the main modes of shape variation and do dimension reduction; A criterion based on the Kullback-Leibler (KL) divergence is proposed to evaluate different hypotheses and models. The KL divergence measures the distribution difference between the beautiful group and the whole population. The results show that male and female faces come from different Gaussian distributions, but the two distributions overlap each other severely. By measuring the KL divergence, it shows that multivariate Gaussian model embodies much more beauty related information than the averageness hypothesis and the symmetry hypothesis. We hope the large-scale database and the proposed evaluation methods can serve as a benchmark for further studies.Department of Computin

Familiarity and face processing

The results of three experiments explore the role of familiarity in face processing. Using the complete-over-part advantage (Experiment 1) and the chimeric faces task (Experiment 2), the results revealed evidence for what may be termed ‘holistic processing’ of unfamiliar, newly-learned, and famous faces. Notably, the extent of holistic processing on both tasks was not moderated by the familiarity of the stimuli. Experiment 3 replicated this pattern using a simultaneous chimeric task to rule out a simple explanation through memory demands. Taken together, these three experiments provide robust and convergent evidence to suggest that all faces regardless of familiarity can be processed in a holistic fashion. On the basis of these results, discussion is presented regarding the value of considering different ‘types’ of facial processing over and above a more simple consideration of task difficulty

Categorical perception effects for facial identity in robustly represented familiar and self-faces: The role of configural and featural information

Categorical perception of robustly represented faces (self, friend) and unfamiliar faces is investigated, and the relative roles of configural and featural information are examined. Participants performed identification and discrimination tasks on morph series containing the self-face and a friend's face (self–Friend 1), two friends' faces (Friend 2–Friend 3), and two unfamiliar faces (Unfamiliar 1–Unfamiliar 2), presented in upright and inverted orientations. For upright faces, categorical perception effects were observed for both familiar morph series but not for the unfamiliar morph series, suggesting that robust representation is a requirement for categorical perception in facial identity. For inverted faces, categorical perception was observed for the self–Friend 1 morph series only. This suggests that categorical perception is tied to configural processing for familiar non-self-faces, but can be observed for self-faces during featural processing—consistent with evidence that self-face representations contain strong configural and featural components. Finally, categorical perception is not enhanced by the presence of the self-face relative to other familiar faces when upright, but shows a trend of being enhanced for self-faces when inverted, adding to the debate on the ways in which robustly represented faces can elicit categorical perception

Spanish basic emotion words are consistently ordered

Basic emotion terms, Facial expressions, Social cognition, Ordinal methods, Computerized data-based research,