Perception of global facial geometry is modulated through experience

Identification of personally familiar faces is highly efficient across various viewing conditions. While the presence of robust facial representations stored in memory is considered to aid this process, the mechanisms underlying invariant identification remain unclear. Two experiments tested the hypothesis that facial representations stored in memory are associated with differential perceptual processing of the overall facial geometry. Subjects who were personally familiar or unfamiliar with the identities presented discriminated between stimuli whose overall facial geometry had been manipulated to maintain or alter the original facial configuration (see Barton, Zhao & Keenan, 2003). The results demonstrate that familiarity gives rise to more efficient processing of global facial geometry, and are interpreted in terms of increased holistic processing of facial information that is maintained across viewing distances

Hemi-field memory for attractiveness

In order to determine whether or not facial attractiveness plays a role in hemispheric facial memory, 35 right-handed participants first assigned attractiveness ratings to faces and then performed a recognition test on those faces in the left visual half-field (LVF) and right visual half-field (RVF). We found significant interactions between the experimental factors and visual half- field. There were significant differences in the extreme ends of the rating scale, that is, the very unattractive versus the very attractive faces: Female participants remembered very attractive faces of both women and men, with memory being superior in the RVF than in the LVF. In contrast, the male participants remembered very unattractive faces of both women and men; RVF memory was better than the LVF for women faces while for men faces memory was superior in the LVF. The interactions with visual half-field suggest that hemispheric biases in remembering faces are influenced by degree of attractiveness

Superior Facial Expression, But Not Identity Recognition, in Mirror-Touch Synesthesia

Simulation models of expression recognition contend that to understand another's facial expressions, individuals map the perceived expression onto the same sensorimotor representations that are active during the experience of the perceived emotion. To investigate this view, the present study examines facial expression and identity recognition abilities in a rare group of participants who show facilitated sensorimotor simulation (mirror-touch synesthetes). Mirror-touch synesthetes experience touch on their own body when observing touch to another person. These experiences have been linked to heightened sensorimotor simulation in the shared-touch network (brain regions active during the passive observation and experience of touch). Mirror-touch synesthetes outperformed nonsynesthetic participants on measures of facial expression recognition, but not on control measures of face memory or facial identity perception. These findings imply a role for sensorimotor simulation processes in the recognition of facial affect, but not facial identity

Embodiment effects in memory for facial identity and facial expression

Research suggests that states of the body, such as postures, facial expressions, and arm movements, play central roles in social information processing. This study investigated the effects of approach/avoidance movements on memory for facial information. Faces displaying a happy or a sad expression were presented and participants were induced to perform either an approach (arm flexion) or an avoidance (arm extension) movement. States of awareness associated with memory for facial identity and memory for facial expression were then assessed with the Remember/Know/Guess paradigm. The results showed that performing avoidance movements increased Know responses for the identity, and Know/Guess responses for the expression, of valence-compatible stimuli (i.e., sad faces as compared to happy faces), whereas this was not the case for approach movements. Based on these findings, it is suggested that approach/avoidance motor actions influence memory encoding by increasing the ease of processing for valence-compatible information

Own attractiveness and perceived relationship quality shape sensitivity in women’s memory for other men on the attractiveness dimension

Although recent work suggests that opposite-sex facial attractiveness is less salient in memory when individuals are in a committed romantic relationship, romantic relationship quality can vary over time. In light of this, we tested whether activating concerns about romantic relationship quality strengthens memory for attractive faces. Partnered women were exposed briefly to faces manipulated in shape cues to attractiveness before either being asked to think about a moment of emotional closeness or distance in their current relationship. We measured sensitivity in memory for faces as the extent to which they recognized correct versions of studied faces over versions of the same person altered to look either more or less-attractive than their original (i.e. studied) version. Contrary to predictions, high relationship quality strengthened hit rate for faces regardless of the sex or attractiveness of the face. In general, women’s memories were more sensitive to attractiveness in women, but were biased toward attractiveness in male faces, both when responding to unfamiliar faces and versions of familiar faces that were more attractive than the original male identity from the learning phase. However, findings varied according to self-rated attractiveness and a psychometric measure of the quality of their current relationship. Attractive women were more sensitive to attractiveness in men, while their less-attractive peers had a stronger bias to remember women as more-attractive and men as less-attractive than their original image respectively. Women in better-quality romantic relationships had stronger positive biases toward, and false memories for, attractive men. Our findings suggest a sophisticated pattern of sensitivity and bias in women’s memory for facial cues to quality that varies systematically according to factors that may alter the costs of female mating competition (‘market demand’) and relationship maintenance

Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved superior performance in micro-expression recognition but at the cost of domain specificity and cumbersome parametric tunings. In this paper, we propose an Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro-expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vector through a Long Short-term Memory (LSTM) module. The framework contains two different network variants: (1) Channel-wise stacking of input data for spatial enrichment, (2) Feature-wise stacking of features for temporal enrichment. We demonstrate that the proposed approach is able to achieve reasonably good performance, without data augmentation. In addition, we also present ablation studies conducted on the framework and visualizations of what CNN "sees" when predicting the micro-expression classes.Comment: Published in Micro-Expression Grand Challenge 2018, Workshop of 13th IEEE Facial & Gesture 201

Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine

In this paper, we propose a scheme that can be used in largescale nonlinear facial image classification problems. An approximate solution of the kernel Extreme Learning Machine classifier is formulated and evaluated. Experiments on twopublicly available facial image datasets using two popular facial image representations illustrate the effectiveness and efficiency of the proposed approach. The proposed Approximate Kernel Extreme Learning Machine classifier is able to scale well in both time and memory, while achieving good generalization performance. Specifically, it is shown that it outperforms the standard ELM approach for the same time and memory requirements. Compared to the original kernel ELM approach, it achieves similar (or better) performance,while scaling well in both time and memory with respect to the training set cardinality