Can We Detect Cooperators by Looking at Their Face?

2017 ◽  
Vol 26 (3) ◽  
pp. 276-281 ◽  
Author(s):  
Jean-François Bonnefon ◽  
Astrid Hopfensitz ◽  
Wim De Neys
Keyword(s):  
Facial Expressions ◽  
Significant Degree ◽  
Body Language ◽  
Facial Features ◽  
Mutual Benefit ◽  
Economic Games ◽  
Video Clips ◽  
Personal Interactions ◽  
Minimal Information ◽  
Better Than

Humans are willing to cooperate with each other for mutual benefit—and to accept the risk of exploitation. To avoid collaborating with the wrong person, people sometimes attempt to detect cooperativeness in others’ body language, facial features, and facial expressions. But how reliable are these impressions? We review the literature on the detection of cooperativeness in economic games, from those with protocols that provide a lot of information about players (e.g., through long personal interactions) to those with protocols that provide minimal information (e.g., through the presentation of passport-like pictures). This literature suggests that people can detect cooperativeness with a small but significant degree of accuracy when they have interacted with or watched video clips of other players, but that they have a harder time extracting information from pictures. The conditions under which people can detect cooperation from pictures with better than chance accuracy suggest that successful cooperation detection is supported by purely intuitive processes.

scholarly journals A simple multi-feature classification to recognize human emotions in Images

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Facial Expressions ◽  
General Pattern ◽  
Facial Feature ◽  
Body Language ◽  
Facial Features ◽  
Verbal Cues ◽  
The Face ◽  
Tone Of Voice

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific<br>facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.<br>

scholarly journals WHO CAN READ YOUR FACIAL EXPRESSION? A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Sunglasses and Facemasks

2021 ◽  
Author(s):  
Harisu Abdullahi Shehu ◽  
Will N. Browne ◽  
Hedwig Eisenbarth
Keyword(s):  
Facial Expressions ◽  
Learning Algorithm ◽  
Facial Features ◽  
Emotional Facial Expressions ◽  
Lower Face ◽  
Machine Learning Classifiers ◽  
Deep Learning Algorithm ◽  
Different Types ◽  
The Impact ◽  
Better Than

Partial face coverings such as sunglasses and facemasks have now become the ‘new norm’, especially since the increase of infectious diseases. Unintentionally, they obscure facial expressions. Therefore, humans and artificial systems have been found to be less accurate in emotion categorization. However, it is unknown how similar the performance of humans compared with artificial systems is affected based on the exact same stimuli, varying systematically in types of coverings. Such a systematic direct comparison would allow conclusions about the relevant facial features in a naturalistic context. Therefore, we investigated the impact of facemasks and sunglasses on the ability to categorize emotional facial expressions in humans and artificial systems. Artificial systems, represented by the VGG19 deep learning algorithm, and humans assessed images of people with varying emotional facial expressions and with four different types of coverings, i.e. unmasked (original images), mask (mask covering lower-face), partial mask (with transparent mouth window), and sunglasses. Artificial systems performed significantly better than humans when no covering is present (&gt; 15% difference). However, the achieved accuracy of both humans and artificial systems differed significantly depending on the type of coverings and, importantly, emotion, e.g. the use of sunglasses reduced accuracy for recognition of fear in humans. It was also noted that while humans mainly classify unknown expressions as neutral across all coverings, the misclassification varied in the artificial systems. These findings show humans and artificial systems classify and misclassify various emotion expressions differently depending on both the type of face covering and type of emotion.

Split-Second Trustworthiness Detection From Faces in an Economic Game

2017 ◽  
Vol 64 (4) ◽  
pp. 231-239 ◽  
Author(s):  
Wim De Neys ◽  
Astrid Hopfensitz ◽  
Jean-François Bonnefon
Keyword(s):  
Exposure Time ◽  
Trust Game ◽  
Facial Features ◽  
Economic Games ◽  
Intuitive Process ◽  
Economic Decisions ◽  
Economic Interactions ◽  
Better Than

Abstract. Economic interactions often imply to gauge the trustworthiness of others. Recent studies showed that when making trust decisions in economic games, people have some accuracy in detecting trustworthiness from the facial features of unknown partners. Here we provide evidence that this face-based trustworthiness detection is a fast and intuitive process by testing its performance at split-second levels of exposure. Participants played a Trust game, in which they made decisions whether to trust another player based on their picture. In two studies, we manipulated the exposure time of the picture. We observed that trustworthiness detection remained better than chance for exposure times as short as 100 ms, although it disappeared with an exposure time of 33 ms. We discuss implications for ongoing debates on the use of facial inferences for social and economic decisions.

scholarly journals A simple multi-feature classification to recognize human emotions in Images

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Facial Expressions ◽  
General Pattern ◽  
Facial Feature ◽  
Body Language ◽  
Facial Features ◽  
Verbal Cues ◽  
The Face ◽  
Tone Of Voice

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific<br>facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.<br>

SIMILARITIES AND DIFFERENT FEATURES OF HAND GESTURES IN AMERICAN AND KYRGYZ CULTURES

2019 ◽  
pp. 19-22
Author(s):  
Avtandil kyzy Ya
Keyword(s):  
Everyday Life ◽  
Facial Expressions ◽  
Main Part ◽  
Body Language ◽  
Hand Gestures ◽  
Common Definition ◽  
The World ◽  
The Common ◽  
Definition Of ◽  
Different Cultures

Abstract: This paper highlights similarities and different features of the category of kinesics “hand gestures”, its frequency usage and acceptance by different individuals in two different cultures. This study shows its similarities, differences and importance of the gestures, for people in both cultures. Consequently, kinesics study was mentioned as a main part of body language. As indicated in the article, the study kinesics was not presented in the Kyrgyz culture well enough, though Kyrgyz people use hand gestures a lot in their everyday life. The research paper begins with the common definition of hand gestures as a part of body language, several handshake categories like: the finger squeeze, the limp fish, the two-handed handshake were explained by several statements in the English and Kyrgyz languages. Furthermore, this article includes definitions and some idioms containing hand, shake, squeeze according to the Oxford and Academic Dictionary to show readers the figurative meanings of these common words. The current study was based on the books of writers Allan and Barbara Pease “The definite book of body language” 2004, Romana Lefevre “Rude hand gestures of the world”2011 etc. Key words: kinesics, body language, gestures, acoustics, applause, paralanguage, non-verbal communication, finger squeeze, perceptions, facial expressions. Аннотация. Бул макалада вербалдык эмес сүйлѳшүүнүн бѳлүгү болуп эсептелген “колдордун жандоо кыймылы”, алардын эки башка маданиятта колдонулушу, айырмачылыгы жана окшош жактары каралган. Макаланын максаты болуп “колдордун жандоо кыймылынын” мааниси, айырмасы жана эки маданиятта колдонулушу эсептелет. Ошону менен бирге, вербалдык эмес сүйлѳшүүнүн бѳлүгү болуп эсептелген “кинесика” илими каралган. Берилген макалада кѳрсѳтүлгѳндѳй, “кинесика” илими кыргыз маданиятында толугу менен изилденген эмес, ошого карабастан “кинесика” илиминин бѳлүгү болуп эсептелген “колдордун жандоо кыймылы” кыргыз элинин маданиятында кѳп колдонулат. Андан тышкары, “колдордун жандоо кыймылынын” бир нече түрү, англис жана кыргыз тилдеринде ма- селен аркылуу берилген.Тѳмѳнкү изилдѳѳ ишин жазууда чет элдик жазуучулардын эмгектери колдонулду. Түйүндүү сѳздѳр: кинесика, жандоо кыймылы, акустика,кол чабуулар, паралингвистика, вербалдык эмес баарлашуу,кол кысуу,кабыл алуу сезими. Аннотация. В данной статье рассматриваются сходства и различия “жестикуляции” и частота ее использования, в американской и кыргызской культурах. Следовательно, здесь было упомянуто понятие “кинесика” как основная часть языка тела. Как указано в статье, “кинесика” не была представлена в кыргызской культуре достаточно хорошо, хотя кыргызский народ часто использует жестикуляцию в повседневной жизни. Исследовательская работа начинается с общего определения “жестикуляции” как части языка тела и несколько категорий жестикуляции, таких как: сжатие пальца, слабое рукопожатие, рукопожатие двумя руками, были объяснены несколькими примерами на английском и кыргызском языках. Кроме того, эта статья включает определения слов “рука”, “рукопожатие”, “сжатие” и некоторые идиомы, содержащие данных слов согласно Оксфордскому и Академическому словарю, чтобы показать читателям их образное значение. Данное исследование было основано на книгах писателей Аллана и Барбары Пиз «Определенная книга языка тела» 2004 года, Романа Лефевра «Грубые жестикуляции мира» 2011 года и т.д. Ключевые слова: кинесика, язык жестов, жесты, акустика, аплодисменты, паралингвистика, невербальная коммуникация, сжатие пальца, чувство восприятия, выражение лиц.

scholarly journals Acting Surprised: Comparing Perceptions of Different Dynamic Deliberate Expressions

2020 ◽  
Author(s):  
Mircea Zloteanu ◽  
Eva G. Krumhuber ◽  
Daniel C. Richardson
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Affective State ◽  
External Condition ◽  
Internal Condition ◽  
Minimal Effort ◽  
Better Than

AbstractPeople are accurate at classifying emotions from facial expressions but much poorer at determining if such expressions are spontaneously felt or deliberately posed. We explored if the method used by senders to produce an expression influences the decoder’s ability to discriminate authenticity, drawing inspiration from two well-known acting techniques: the Stanislavski (internal) and Mimic method (external). We compared spontaneous surprise expressions in response to a jack-in-the-box (genuine condition), to posed displays of senders who either focused on their past affective state (internal condition) or the outward expression (external condition). Although decoders performed better than chance at discriminating the authenticity of all expressions, their accuracy was lower in classifying external surprise compared to internal surprise. Decoders also found it harder to discriminate external surprise from spontaneous surprise and were less confident in their decisions, perceiving these to be similarly intense but less genuine-looking. The findings suggest that senders are capable of voluntarily producing genuine-looking expressions of emotions with minimal effort, especially by mimicking a genuine expression. Implications for research on emotion recognition are discussed.

Aircraft Applications of Insecticides in East Africa. VIII.—An Experiment against the Tsetse Fly, Glossina swynnertoni Aust., in an isolated Area of Thronbush and Thicket

1954 ◽  
Vol 45 (3) ◽  
pp. 613-622 ◽  
Author(s):  
K. S. Hocking ◽  
G. F. Burnett ◽  
R. C. Sell
Keyword(s):  
East Africa ◽  
Apparent Density ◽  
Significant Degree ◽  
Tsetse Fly ◽  
Central Province ◽  
The North ◽  
Insecticidal Treatment ◽  
Bad Weather ◽  
Glossina Swynnertoni ◽  
Better Than

An isolated area of 2,200 acres of thicket and thronbush in the Central Province, Tanganyika, was treated from the air with a DDT-in-oil aerosol in an attempt to eliminate the tsetse fly, Glossina swynnertoni Aust. Eight applications of 0·25 lb. technical DDT per acre were planned to be done at fortnightly intervals.Delays due to unseasonal bad weather reduced this to seven at a slightly higher rate and over a longer-period.G. swynnertoni was reduced from an apparent density of about 7 to zero at the end of the second application. No flies were caught after the fifth application for a period of six months.It is not possible to say whether the few caught since then have been brought in or are the offspring of survivors of the insecticidal treatment.This experiment was more successful than that on the Galapo Block in the same ares, to a highly significant degree, and this is attributed to the vulnerability of the smaller population present. It was doubtfully better than the first treatment of the North Block, also in this area, because the increase in population in the latter block may have been assisted by immigration.

The biasing effects of appearances go beyond physical attractiveness and mating motives

2017 ◽  
Vol 40 ◽  
Author(s):  
Christopher Y. Olivola ◽  
Alexander Todorov
Keyword(s):  
Labor Markets ◽  
Physical Attractiveness ◽  
Facial Features ◽  
Economic Games ◽  
Experimental Economic ◽  
Growing Body ◽  
Social Attributions ◽  
Biasing Effects

AbstractThe influence of appearances goes well beyond physical attractiveness and includes the surprisingly powerful impact of “face-ism” – the tendency to stereotype individuals based on their facial features. A growing body of research has revealed that these face-based social attributions bias the outcomes of labor markets and experimental economic games in ways that are hard to explain via evolutionary mating motives.

scholarly journals COMPARATIVE STUDY OF USING DRAMA AND SIMULATION TECHNIQUES TOWARD STUDENTS’ SPEAKING ABILITY

2017 ◽  
Vol 1 ◽  
pp. 208
Author(s):  
Eva Faliyanti ◽  
Nanda Habib Firdaus
Keyword(s):  
Hypothesis Test ◽  
Analysis Data ◽  
Significant Degree ◽  
Simulation Technique ◽  
Control Group ◽  
Control Group Design ◽  
Systematic Random Sampling ◽  
Simulation Techniques ◽  
Post Test ◽  
Better Than

Speaking is one of the productive skills in English. It acts as an important skill which becomes a depiction of students' result in studying English. Oftentimes, English ability is measured by their speaking because it serves a tool of communication which represents the major interaction with other people. But in fact, the condition of a learning process in a sphere of speaking has not supported students to master speaking ability yet. The application of techniques and methods are actually not interesting enough to attract them. Students tend to be passive when they are in learning about material related to speaking.The objectives of this research are; to find out students' speaking score by applying drama technique; to find out students' score by using simulation technique; to find out whether the drama is better than simulation technique in increasing students' speaking ability. The population of this research is 37 students in the fourth semester of English Department at the Muhammadiyah University of Metro. The researcher conducts systematic random sampling design and pre-test and post-test control group design as a research instrument. The result of hypothesis test on test phases both provides the different result which uses 5% significant degree, where a count is higher than t -table on the analysis data of post-test with t-count =2,36  and t-table =2,12. It shows that the students who are taught by drama technique having a higher score. In conclusion that drama technique is more effective than simulation technique in term of students' speaking ability.

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