In recent decades,the potential health hazards of microwave exposure have been attracting increasing attention.Our previous studies have demonstrated that microwave exposure impaired learning and memory in experimenta...In recent decades,the potential health hazards of microwave exposure have been attracting increasing attention.Our previous studies have demonstrated that microwave exposure impaired learning and memory in experimental animal models[1,2].展开更多
1 Introduction.Micro-expressions(MEs)are characterized by their involuntary and brief display,typically lasting less than 0.5 seconds[1].Due to their uncontrollable nature,MEs play a significant role in fields such as...1 Introduction.Micro-expressions(MEs)are characterized by their involuntary and brief display,typically lasting less than 0.5 seconds[1].Due to their uncontrollable nature,MEs play a significant role in fields such as business negotiations,criminal investigations,and disease diagnosis.展开更多
The human face forms a canvas wherein various non-verbal expressions are communicated.These expressional cues and verbal communication represent the accurate perception of the actual intent.In many cases,a person may ...The human face forms a canvas wherein various non-verbal expressions are communicated.These expressional cues and verbal communication represent the accurate perception of the actual intent.In many cases,a person may present an outward expression that might differ fromthe genuine emotion or the feeling that the person experiences.Even when people try to hide these emotions,the real emotions that are internally felt might reflect as facial expressions in the form of micro expressions.These micro expressions cannot be masked and reflect the actual emotional state of a person under study.Suchmicro expressions are on display for a tiny time frame,making it difficult for a typical person to spot and recognize them.This necessitates a place for Machine Learning,where machines can be trained to look for these micro expressions and categorize them once they are on display.The study’s primary purpose is to spot and correctly classify these micro expressions,which are very difficult for a casual observer to identify.This research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning,cropping,and superimposing these feature frames to produce highly accurate and consistent results.A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85%in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less accuracy.One of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions,which significantly increases the accuracy of the outcomes.The proposed swarm-based technique handles this in the research to precisely align and crop the subsequent frames,resulting in much superior detection rates in identifying the micro expressions when on display.展开更多
Research has demonstrated a relationship between anger and suicidality, while real-timeauthentic emotions behind facial expressions could be detected during advising hypotheticalprotagonists in life dilemmas. This stu...Research has demonstrated a relationship between anger and suicidality, while real-timeauthentic emotions behind facial expressions could be detected during advising hypotheticalprotagonists in life dilemmas. This study aimed to investigate the predictive validity ofanger expressions during advising for suicide risk. Besides advising on life dilemmas(a friend’s betrayal, a friend’s suicide attempt), 130 adults completed the suicidal scale ofthe Mini-International Neuropsychiatric Interview. Participants’ anger during advicegivingwas measured 29 times/s by artificial intelligence (AI)-based software FaceReader7.1. The results showed that anger was a significant predictor of suicide risk. Increasedanger during advising was associated with higher suicide risk. In contrast, there was no significantcorrelation between suicide risk and duration or length of advising. Therefore,measuring micro expressions of anger with AI-based software may help detect suicide riskamong clinical patients in both traditional and online counseling contexts and help preventsuicide.展开更多
基金supported by National Science Foundation of China[No.81172620]。
文摘In recent decades,the potential health hazards of microwave exposure have been attracting increasing attention.Our previous studies have demonstrated that microwave exposure impaired learning and memory in experimental animal models[1,2].
基金supported by the National Key Research and Development Program of China(2022YFC3301800)the Provincial Key Research and Development Program of Heilongjiang(Grant No.GA21C022)the Independent Research Exploration Projects of Songjiang Laboratory(Grant No.SL20230203).
文摘1 Introduction.Micro-expressions(MEs)are characterized by their involuntary and brief display,typically lasting less than 0.5 seconds[1].Due to their uncontrollable nature,MEs play a significant role in fields such as business negotiations,criminal investigations,and disease diagnosis.
文摘The human face forms a canvas wherein various non-verbal expressions are communicated.These expressional cues and verbal communication represent the accurate perception of the actual intent.In many cases,a person may present an outward expression that might differ fromthe genuine emotion or the feeling that the person experiences.Even when people try to hide these emotions,the real emotions that are internally felt might reflect as facial expressions in the form of micro expressions.These micro expressions cannot be masked and reflect the actual emotional state of a person under study.Suchmicro expressions are on display for a tiny time frame,making it difficult for a typical person to spot and recognize them.This necessitates a place for Machine Learning,where machines can be trained to look for these micro expressions and categorize them once they are on display.The study’s primary purpose is to spot and correctly classify these micro expressions,which are very difficult for a casual observer to identify.This research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning,cropping,and superimposing these feature frames to produce highly accurate and consistent results.A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85%in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less accuracy.One of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions,which significantly increases the accuracy of the outcomes.The proposed swarm-based technique handles this in the research to precisely align and crop the subsequent frames,resulting in much superior detection rates in identifying the micro expressions when on display.
基金National Natural Science Foundation of China,Grant/Award Number:31800905。
文摘Research has demonstrated a relationship between anger and suicidality, while real-timeauthentic emotions behind facial expressions could be detected during advising hypotheticalprotagonists in life dilemmas. This study aimed to investigate the predictive validity ofanger expressions during advising for suicide risk. Besides advising on life dilemmas(a friend’s betrayal, a friend’s suicide attempt), 130 adults completed the suicidal scale ofthe Mini-International Neuropsychiatric Interview. Participants’ anger during advicegivingwas measured 29 times/s by artificial intelligence (AI)-based software FaceReader7.1. The results showed that anger was a significant predictor of suicide risk. Increasedanger during advising was associated with higher suicide risk. In contrast, there was no significantcorrelation between suicide risk and duration or length of advising. Therefore,measuring micro expressions of anger with AI-based software may help detect suicide riskamong clinical patients in both traditional and online counseling contexts and help preventsuicide.
