Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression

Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance

Island Loss for Learning Discriminative Features in Facial Expression Recognition

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.Comment: 8 pages, 3 figure

Maori facial tattoo (Ta Moko): implications for face recognition processes.

Ta Moko is the art of the Maori tattoo. It was an integral aspect of Maori society and is currently seeing resurgence in popularity. In particular it is linked with ancestry and a sense of “Maori” pride. Ta Moko is traditionally worn by Maori males on the buttocks and on the face, while Maori women wear it on the chin and lips. With curvilinear lines and spiral patterns applied to the face with a dark pigment, the full facial Moko creates a striking appearance. Given our reliance on efficiently encoding faces this transformation could potentially interfere with how viewers normally process and recognise the human face (e.g. configural information). The pattern’s effects on recognising identity, expression, race, speech, and gender are considered, and implications are drawn, which could help wearers and viewers of Ta Moko understand why sustained attention (staring) is drawn to such especially unique faces

Interaction of HPA axis genetics and early life stress shapes emotion recognition in healthy adults

Background: Early life stress (ELS) affects facial emotion recognition (FER), as well as the underlying brain network. However, there is considerable inter-individual variability in these ELS-caused alterations. As the hypothalamic-pituitary-adrenal (HPA) axis is assumed to mediate neural and behavioural sequelae of ELS, the genetic disposition towards HPA axis reactivity might explain differential vulnerabilities. Methods: An additive genetic profile score (GPS) of HPA axis reactivity was built from 6 SNPs in 3 HPA axisrelated genes (FKBP5, CRHR1, NR3C1). We studied two independent samples. As a proof of concept, GPS was tested as a predictor of cortisol increase to a psychosocial challenge (MIST) in a healthy community sample of n=40. For the main study, a sample of n=170 completed a video-based FER task and retrospectively reported ELS experiences in the Childhood Trauma Questionnaire (CTQ). Results: GPS positively predicted cortisol increase in the stress challenge over and above covariates. CTQ and genetic profile scores interacted to predict facial emotion recognition, such that ELS had a detrimental effect on emotion processing only in individuals with higher GPS. Post-hoc moderation analyses revealed that, while a less stress-responsive genetic profile was protective against ELS effects, individuals carrying a moderate to high GPS were affected by ELS in their ability to infer emotion from facial expressions. Discussion: These results suggest that a biologically informed genetic profile score can capture the genetic disposition to HPA axis reactivity and moderates the influence of early environmental factors on facial emotion recognition. Further research should investigate the neural mechanisms underlying this moderation. The GPS used here might prove a powerful tool for studying inter-individual differences in vulnerability to early life stress