Human Centric Facial Expression Recognition

Facial expression recognition (FER) is an area of active research, both in computer science and in behavioural science. Across these domains there is evidence to suggest that humans and machines find it easier to recognise certain emotions, for example happiness, in comparison to others. Recent behavioural studies have explored human perceptions of emotion further, by evaluating the relative contribution of features in the face when evaluating human sensitivity to emotion. It has been identified that certain facial regions have more salient features for certain expressions of emotion, especially when emotions are subtle in nature. For example, it is easier to detect fearful expressions when the eyes are expressive. Using this observation as a starting point for analysis, we similarly examine the effectiveness with which knowledge of facial feature saliency may be integrated into current approaches to automated FER. Specifically, we compare and evaluate the accuracy of ‘full-face’ versus upper and lower facial area convolutional neural network (CNN) modelling for emotion recognition in static images, and propose a human centric CNN hierarchy which uses regional image inputs to leverage current understanding of how humans recognise emotions across the face. Evaluations using the CK+ dataset demonstrate that our hierarchy can enhance classification accuracy in comparison to individual CNN architectures, achieving overall true positive classification in 93.3% of cases

Seven- to 11-year-olds’ developing ability to recognize natural facial expressions of basic emotions

Being able to recognize facial expressions of basic emotions is of great importance to social development. However, we still know surprisingly little about children’s developing ability to interpret emotions that are expressed dynamically, naturally and subtly, despite real-life expressions having such appearance in the vast majority of cases. The current research employs a new technique of capturing dynamic, subtly expressed natural emotional displays (happy, sad, angry, shocked and disgusted). Children aged 7, 9 and 11 years (and adults) were systematically able to discriminate each emotional display from alternatives in a 5-way choice. Children were most accurate in identifying the expression of happiness and were also relatively accurate in identifying the expression of sadness; they were far less accurate than adults in identifying shocked and disgusted. Children who performed well academically also tended to be the most accurate in recognizing expressions and this relationship maintained independently of chronological age. Generally, the findings testify to a well-developed ability to recognize very subtle naturally occurring expressions of emotions

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

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