scholarly journals Facial Emotions over Static Facial Images Using Deep Learning Techniques with Hysterical Interpretation

2021 ◽  
Vol 2089 (1) ◽  
pp. 012014
Author(s):  
Dr A ViswanathReddy ◽  
A Aswini Reddy ◽  
C A Bindyashree
Keyword(s):  
Reinforcement Learning ◽  
Positive Emotions ◽  
Computation Time ◽  
Facial Emotions ◽  
Mouth Region ◽  
White Pixel ◽  
Potential Applications ◽  
The Face ◽  
Trial And Error Method ◽  
Class Labels

Abstract Recognition of facial expression has many potential applications that have attracted the researcher’s attention during the last decade. Taking out of features, is an important step in the analysis of expression that contributes to a quick and accurate recognition of expression, i.e., happiness, surprise and disgust, sadness, anger, and fear are expressions of the faces. Facial expressions are most frequently used to interpret human emotions. Two categories contain a range of different emotions: positive emotions and non-positive emotions. Face Detection, Extraction, Classification, and Recognition are major steps used in the proposed system. The proposed segmentation techniques are applied and compared to determine which method is appropriate for splitting the mouth region, and then the mouth region can be extracted using techniques for stretching contrasts and segmenting the image. After the extraction of the mouth area, the facial emotions are graded in the face picture region of the extracted mouth based on white pixel values. The Supervisory Learning Approach is widely used for face identification algorithms and it takes more computation time and effort. It may also give incorrect class labels in the classification process. For this reason, supervised learning and reinforcement learning is being used. In general, it will be like a trial-and-error method that is, in the training process it tries to learn and produce expected results. It was specified accordingly. Reinforcement learning always tries to enhance the results.

External sources promoting resilience in adults with intellectual disabilities: A systematic literature review

2020 ◽  
pp. 174462952096194 ◽  
Author(s):  
Femke Scheffers ◽  
Xavier Moonen ◽  
Eveline van Vugt
Keyword(s):  
Social Network ◽  
Literature Review ◽  
Intellectual Disabilities ◽  
Positive Emotions ◽  
Network Support ◽  
Increased Risk ◽  
The Social ◽  
The Face ◽  
External Sources ◽  
Adults With Intellectual Disabilities

Background: Persons with an intellectual disability are at increased risk of experiencing adversities. The current study aims at providing an overview of the research on how resilience in adults with intellectual disabilities, in the face of adversity, is supported by sources in their social network. Method: A literature review was conducted in the databases Psycinfo and Web of Science. To evaluate the quality of the included studies, the Mixed Method Appraisal Tool (MMAT) was used. Results: The themes: “ positive emotions,” “ network acceptance,” “ sense of coherence” and “ network support,” were identified as sources of resilience in the social network of the adults with intellectual disabilities. Conclusion: The current review showed that research addressing sources of resilience among persons with intellectual disabilities is scarce. In this first overview, four sources of resilience in the social network of people with intellectual disabilities were identified that interact and possibly strengthen each other.

scholarly journals MEG signatures of long-term effects of agreement and disagreement with the majority

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Gorin ◽  
V. Klucharev ◽  
A. Ossadtchi ◽  
I. Zubarev ◽  
V. Moiseeva ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Social Influence ◽  
Temporal Dynamics ◽  
Peer Group ◽  
Long Term Effects ◽  
Learning Mechanisms ◽  
Source Imaging ◽  
The Face ◽  
First Session

AbstractPeople often change their beliefs by succumbing to an opinion of others. Such changes are often referred to as effects of social influence. While some previous studies have focused on the reinforcement learning mechanisms of social influence or on its internalization, others have reported evidence of changes in sensory processing evoked by social influence of peer groups. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the long-term effects of agreement and disagreement with the peer group. The study was composed of two sessions. During the first session, participants rated the trustworthiness of faces and subsequently learned group rating of each face. In the first session, a neural marker of an immediate mismatch between individual and group opinions was found in the posterior cingulate cortex, an area involved in conflict-monitoring and reinforcement learning. To identify the neural correlates of the long-lasting effect of the group opinion, we analysed MEG activity while participants rated faces during the second session. We found MEG traces of past disagreement or agreement with the peers at the parietal cortices 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger when the participant’s rating had previously differed from the ratings of the peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of long-term effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex.

scholarly journals Tag N’ Train: a technique to train improved classifiers on unlabeled data

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Oz Amram ◽  
Cristina Mantilla Suarez
Keyword(s):  
Real Data ◽  
Unlabeled Data ◽  
Machine Learning Techniques ◽  
Jet Physics ◽  
Classification Problems ◽  
Weak Classifier ◽  
Potential Applications ◽  
Substantial Progress ◽  
Resonance Search ◽  
Class Labels

Abstract There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets that may not be well modeled in simulation. Therefore, relying on simulations for training will lead to sub-optimal performance in data, but the lack of true class labels makes it difficult to train on real data. To address this challenge we introduce a new approach, called Tag N’ Train (TNT), that can be applied to unlabeled data that has two distinct sub-objects. The technique uses a weak classifier for one of the objects to tag signal-rich and background-rich samples. These samples are then used to train a stronger classifier for the other object. We demonstrate the power of this method by applying it to a dijet resonance search. By starting with autoencoders trained directly on data as the weak classifiers, we use TNT to train substantially improved classifiers. We show that Tag N’ Train can be a powerful tool in model-agnostic searches and discuss other potential applications.

scholarly journals The spatial distribution of eye movements predicts the (false) recognition of emotional facial expressions

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245777
Author(s):  
Fanny Poncet ◽  
Robert Soussignan ◽  
Margaux Jaffiol ◽  
Baptiste Gaudelus ◽  
Arnaud Leleu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Eye Movements ◽  
Eye Tracking ◽  
Facial Expressions ◽  
False Recognition ◽  
Recognition Task ◽  
Visual Exploration ◽  
Emotional Expressions ◽  
Facial Emotions ◽  
The Face

Recognizing facial expressions of emotions is a fundamental ability for adaptation to the social environment. To date, it remains unclear whether the spatial distribution of eye movements predicts accurate recognition or, on the contrary, confusion in the recognition of facial emotions. In the present study, we asked participants to recognize facial emotions while monitoring their gaze behavior using eye-tracking technology. In Experiment 1a, 40 participants (20 women) performed a classic facial emotion recognition task with a 5-choice procedure (anger, disgust, fear, happiness, sadness). In Experiment 1b, a second group of 40 participants (20 women) was exposed to the same materials and procedure except that they were instructed to say whether (i.e., Yes/No response) the face expressed a specific emotion (e.g., anger), with the five emotion categories tested in distinct blocks. In Experiment 2, two groups of 32 participants performed the same task as in Experiment 1a while exposed to partial facial expressions composed of actions units (AUs) present or absent in some parts of the face (top, middle, or bottom). The coding of the AUs produced by the models showed complex facial configurations for most emotional expressions, with several AUs in common. Eye-tracking data indicated that relevant facial actions were actively gazed at by the decoders during both accurate recognition and errors. False recognition was mainly associated with the additional visual exploration of less relevant facial actions in regions containing ambiguous AUs or AUs relevant to other emotional expressions. Finally, the recognition of facial emotions from partial expressions showed that no single facial actions were necessary to effectively communicate an emotional state. In contrast, the recognition of facial emotions relied on the integration of a complex set of facial cues.

scholarly journals A nonlinear relationship between prediction errors and learning rates in human reinforcement learning

2019 ◽  
Author(s):  
Erdem Pulcu
Keyword(s):  
Reinforcement Learning ◽  
Nonlinear Relationship ◽  
Prediction Errors ◽  
Learning Rates ◽  
The Face ◽  
In The Wild ◽  
Actual Outcome ◽  
Update Rules ◽  
Different Sources

AbstractWe are living in a dynamic world in which stochastic relationships between cues and outcome events create different sources of uncertainty1 (e.g. the fact that not all grey clouds bring rain). Living in an uncertain world continuously probes learning systems in the brain, guiding agents to make better decisions. This is a type of value-based decision-making which is very important for survival in the wild and long-term evolutionary fitness. Consequently, reinforcement learning (RL) models describing cognitive/computational processes underlying learning-based adaptations have been pivotal in behavioural2,3 and neural sciences4–6, as well as machine learning7,8. This paper demonstrates the suitability of novel update rules for RL, based on a nonlinear relationship between prediction errors (i.e. difference between the agent’s expectation and the actual outcome) and learning rates (i.e. a coefficient with which agents update their beliefs about the environment), that can account for learning-based adaptations in the face of environmental uncertainty. These models illustrate how learners can flexibly adapt to dynamically changing environments.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

scholarly journals To Be Happy: A Case Study of Entrepreneurial Motivation and Entrepreneurial Process from the Perspective of Positive Psychology

2020 ◽  
Vol 12 (2) ◽  
pp. 584 ◽  
Author(s):  
Xiaohua Su ◽  
Shengmei Liu ◽  
Shujun Zhang ◽  
Lingling Liu
Keyword(s):  
Positive Psychology ◽  
Positive Emotion ◽  
Positive Emotions ◽  
Cognitive Structure ◽  
Entrepreneurial Intention ◽  
Entrepreneurial Process ◽  
Entrepreneurial Motivation ◽  
Wealth Maximization ◽  
The Face ◽  
New Perspective

The pursuit of wealth maximization is considered to be the greatest driving force of entrepreneurship. However, this economic rational perspective cannot sufficiently answer why potential or continuous entrepreneurs still choose entrepreneurship or even continuous entrepreneurship in the face of high failure rate and tremendous uncertainty. On the basis of the dynamic process of entrepreneurship and the perspective of positive psychology, this study attempts to interpret the sustained motivation mechanism of entrepreneurs. This study uses multiple cases to investigate the emotion, cognition, and behavior of entrepreneurial process. Through NVivo software and emotion dictionary, more than 27,000 micro blogs (Weibo) of six entrepreneurs were analyzed, and the model of positive emotion in entrepreneurial process was constructed. The findings are as follows. (1) In the process of establishing a business, entrepreneurs can persist in a highly uncertain environment by acquiring positive emotions. That is, the motivation of sustainable entrepreneurship originates from the emotion of happiness and satisfaction that entrepreneurs obtain. (2) Positive emotions affect the formation and expansion of key activities of entrepreneurship through cognition and then persist with entrepreneurship. Specifically, positive emotion promotes the formation of entrepreneurial intention by expanding cognitive structure, intuitive processing, and analytical processing to promote the acquisition of entrepreneurial resources and the expansion of entrepreneurial ability. (3) In the process of entrepreneurship, emotional return is a performance dimension parallel to economic return. This conclusion provides a new perspective towards revealing the entrepreneurial motivation of entrepreneurs in highly ambiguous environments.

scholarly journals Vigor in the Face of Fluctuating Rates of Reward: An Experimental Examination

2011 ◽  
Vol 23 (12) ◽  
pp. 3933-3938 ◽  
Author(s):  
Marc Guitart-Masip ◽  
Ulrik R. Beierholm ◽  
Raymond Dolan ◽  
Emrah Duzel ◽  
Peter Dayan
Keyword(s):  
Reinforcement Learning ◽  
Computational Models ◽  
Average Rate ◽  
Empirical Studies ◽  
Empirical Work ◽  
Healthy Human ◽  
Average Return ◽  
The Face ◽  
Cost Of Time ◽  
Human Participants

Two fundamental questions underlie the expression of behavior, namely what to do and how vigorously to do it. The former is the topic of an overwhelming wealth of theoretical and empirical work particularly in the fields of reinforcement learning and decision-making, with various forms of affective prediction error playing key roles. Although vigor concerns motivation, and so is the subject of many empirical studies in diverse fields, it has suffered a dearth of computational models. Recently, Niv et al. [Niv, Y., Daw, N. D., Joel, D., & Dayan, P. Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacology (Berlin), 191, 507–520, 2007] suggested that vigor should be controlled by the opportunity cost of time, which is itself determined by the average rate of reward. This coupling of reward rate and vigor can be shown to be optimal under the theory of average return reinforcement learning for a particular class of tasks but may also be a more general, perhaps hard-wired, characteristic of the architecture of control. We, therefore, tested the hypothesis that healthy human participants would adjust their RTs on the basis of the average rate of reward. We measured RTs in an odd-ball discrimination task for rewards whose magnitudes varied slowly but systematically. Linear regression on the subjects' individual RTs using the time varying average rate of reward as the regressor of interest, and including nuisance regressors such as the immediate reward in a round and in the preceding round, showed that a significant fraction of the variance in subjects' RTs could indeed be explained by the rate of experienced reward. This validates one of the key proposals associated with the model, illuminating an apparently mandatory form of coupling that may involve tonic levels of dopamine.

Embodied Emotion Perception

2011 ◽  
Vol 2 (6) ◽  
pp. 673-678 ◽  
Author(s):  
David T. Neal ◽  
Tanya L. Chartrand
Keyword(s):  
Emotion Perception ◽  
Muscle Contractions ◽  
Dermal Filler ◽  
Facial Feedback ◽  
Cosmetic Procedure ◽  
Facial Emotions ◽  
Embodied Emotion ◽  
The Face ◽  
Source Of Information

How do we recognize the emotions other people are feeling? One source of information may be facial feedback signals generated when we automatically mimic the expressions displayed on others' faces. Supporting this “embodied emotion perception,” dampening (Experiment 1) and amplifying (Experiment 2) facial feedback signals, respectively, impaired and improved people’s ability to read others' facial emotions. In Experiment 1, emotion perception was significantly impaired in people who had received a cosmetic procedure that reduces muscular feedback from the face (Botox) compared to a procedure that does not reduce feedback (a dermal filler). Experiment 2 capitalized on the fact that feedback signals are enhanced when muscle contractions meet resistance. Accordingly, when the skin was made resistant to underlying muscle contractions via a restricting gel, emotion perception improved, and did so only for emotion judgments that theoretically could benefit from facial feedback.

scholarly journals Human Face and Facial Parts Detection using Template Matching Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 2296-2299
Keyword(s):  
Face Recognition ◽  
Template Matching ◽  
Cross Correlation ◽  
Human Face ◽  
Target Image ◽  
Matching Technique ◽  
Potential Applications ◽  
The Face ◽  
Execution Speed ◽  
Angle Interval

Face recognition has become relevant in recent years because of its potential applications. The aim of this paper is to find out the relevant techniques which give not only better accuracy also the efficient speed. There are several techniques available for face detection which give much better accuracy but the execution speed is not efficient. In this paper, a normalized cross-correlation template matching technique is used to solve this problem. According to the proposed algorithm, first different facial parts are detected likes mouth, eyes, and nose. If any of the two facial parts are found successfully then the face can be detected. For matching the templates with the target image, the template rotates at a certain angle interval.

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