Assessing the Big Five personality traits using real-life static facial images

Predicting personality or prejudice? Facial inference in the age of artificial intelligence

Facial inference, a cornerstone of person perception, has traditionally been studied through human judgments about personality traits and abilities based on people's faces. Recent advances in artificial intelligence (AI) have introduced new dimensions to this field, employing machine learning algorithms to reveal people's character, capabilities, and social outcomes based just on their faces. This review examines recent research on human and AI-based facial inference across psychology, business, computer science, legal, and policy studies to highlight the need for scientific consensus on whether or not people's faces can reveal their inner traits, and urges researchers to address the critical concerns around epistemic validity, practical relevance, and societal welfare before recommending AI-based facial inference for consequential uses.

Personality of organizational social media accounts and its relationship with characteristics of their photos: analyses of startups' Instagram photos

BACKGROUND

Organizational accounts of social networking sites (SNSs) are similar to individual accounts in terms of their online behaviors. Thus, they can be investigated from the perspective of personality, as individual accounts have been in the literature. Focusing on startups' Instagram accounts, this study aimed to investigate the characteristics of Big Five personality traits and the relationships between the traits and the characteristics of photos in organizational SNS accounts.

METHODS

The personality traits of 108 startups' accounts were assessed with an online artificial intelligence service, and a correspondence analysis was performed to identify the key dimensions where the account were distributed by their personality. Photo features were extracted at the content and pixel levels, and correlational analyses between personality traits and photo features were conducted. Moreover, predictive analyses were performed using random forest regression models.

RESULTS

The results indicated that personality of the accounts had high openness, agreeableness, and conscientiousness and moderate extraversion and neuroticism. In addition, the two dimensions of high vs. low in neuroticism and extraversion/openness vs. conscientiousness/agreeableness in the accounts' distribution by their personality traits were identified. Conscientiousness was the trait most associated with photo features-in particular, with content category, pixel-color, and visual features, while agreeableness was the trait least associated with photo features. Neuroticism was mainly correlated with pixel-level features, openness was correlated mainly with pixel-color features, and extraversion was correlated mainly with facial features. The personality traits, except neuroticism, were predicted from the photo features.

CONCLUSIONS

This study applied the theoretical lens of personality, which has been mainly used to examine individuals' behaviors, to investigate the SNS communication of startups. Moreover, it focused on the visual communication of organizational accounts, which has not been actively studied in the literature. This study has implications for expanding the realm of personality research to organizational SNS accounts.

A Richer Vocabulary of Chinese Personality Traits: Leveraging Word Embedding Technology for Mining Personality Descriptors

This study uses a data-driven approach to mine the distribution of personality traits among Chinese people in the Chinese social context. Based on the hypothesis of personality lexicology, word embedding technology was employed in machine learning to mine personality vocabulary from Tencent's word embedding database. More than 10,000 Chinese personality descriptors were extracted and analyzed using Gaussian Mixture Model Cluster and Hierarchical clustering analysis. The data was collected from 658 Chinese people randomly from all parts of China through an online questionnaire method. The results reveal six personality traits in the Chinese context, expanding the personality thesaurus and providing examples to illustrate each trait. The findings coincide with previous research on the five-factor model, which partially describes the personality traits of Chinese people, but does not offer a complete explanation of their typical social behavior patterns. Additionally, the study supports the notion of cultural particularity in personality traits. The approach used in this study offers a richer personality vocabulary than traditional personality mining methods, and word embedding technology captures richer semantic information in Chinese. The six Chinese personality traits identified in this study will also be used to explore how to quantify and evaluate personality traits based on word embedding and personality descriptors.

Artificial intelligence and social intelligence: preliminary comparison study between AI models and psychologists

Background

Social intelligence (SI) is of great importance in the success of the counseling and psychotherapy, whether for the psychologist or for the artificial intelligence systems that help the psychologist, as it is the ability to understand the feelings, emotions, and needs of people during the counseling process. Therefore, this study aims to identify the Social Intelligence (SI) of artificial intelligence represented by its large linguistic models, "ChatGPT; Google Bard; and Bing" compared to psychologists.

Methods

A stratified random manner sample of 180 students of counseling psychology from the bachelor's and doctoral stages at King Khalid University was selected, while the large linguistic models included ChatGPT-4, Google Bard, and Bing. They (the psychologists and the AI models) responded to the social intelligence scale.

Results

There were significant differences in SI between psychologists and AI's ChatGPT-4 and Bing. ChatGPT-4 exceeded 100% of all the psychologists, and Bing outperformed 50% of PhD holders and 90% of bachelor's holders. The differences in SI between Google Bard and bachelor students were not significant, whereas the differences with PhDs were significant; Where 90% of PhD holders excel on Google Bird.

Conclusion

We explored the possibility of using human measures on AI entities, especially language models, and the results indicate that the development of AI in understanding emotions and social behavior related to social intelligence is very rapid. AI will help the psychotherapist a great deal in new ways. The psychotherapist needs to be aware of possible areas of further development of AI given their benefits in counseling and psychotherapy. Studies using humanistic and non-humanistic criteria with large linguistic models are needed.

Facial Expression of TIPI Personality and CHMP-Tri Psychopathy Traits in Chimpanzees (Pan troglodytes) : Evidence for Honest Signalling?

Honest signalling theory suggests that humans and chimpanzees can extract socially relevant information relating to personality from the faces of their conspecifics. Humans are also able to extract information from chimpanzees' faces. Here, we examine whether personality characteristics of chimpanzees, including measures of psychopathy, can be discerned based purely on facial morphology in photographs. Twenty-one chimpanzees were given naïve and expert personality ratings on the Ten Item Personality Inventory (TIPI) and the Chimpanzee Triarchic Model of Psychopathy (CHMP-Tri) before and following behavioural observations. Characteristics relating to openness, conscientiousness, extraversion, and disinhibition could be distinguished from the faces of chimpanzees. Individuals higher on disinhibition have lower scores on conscientiousness and emotional stability and higher scores on extraversion, while those higher on meanness have lower conscientiousness and agreeableness. Facial expressions are linked to personality traits present in the TIPI and CHMP-Tri models: the Relaxed Face and the Grooming Face were displayed more by chimpanzees higher on agreeableness, whereas the Compressed Lips Face was observed more in those individuals higher on boldness, and the Full Open Grin was displayed more by chimpanzees higher on extraversion but lower on emotional stability and conscientiousness. Facial expressions were also found to be associated with particular behavioural contexts, namely the Grooming Face in affiliative contexts and the Relaxed and Relaxed Open Mouth Faces in neutral contexts. Dominant chimpanzees display higher levels of boldness and more Compressed Lips Faces, Relaxed Open Mouth Faces, and Grooming Faces than subordinate individuals. These findings support and extend evidence for an honest signalling system and a personality structure shared between humans and chimpanzees. Future research could further explore how personality is conveyed through the face, perhaps through more than just singular aspects of character, and maybe reflecting what chimpanzees themselves are able to do.

Py-Feat: Python Facial Expression Analysis Toolbox

Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state-of-the-art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-023-00191-4.

Unravelling the many facets of human cooperation in an experimental study

Humans readily cooperate, even with strangers and without prospects of reciprocation. Despite thousands of studies, this finding is not well understood. Most studies focussed on a single aspect of cooperation and were conducted under anonymous conditions. However, cooperation is a multi-faceted phenomenon, involving generosity, readiness to share, fairness, trust, trustworthiness, and willingness to take cooperative risks. Here, we report findings of an experiment where subjects had to make decisions in ten situations representing different aspects of cooperation, both under anonymous and 'personalised' conditions. In an anonymous setting, we found considerable individual variation in each decision situation, while individuals were consistent both within and across situations. Prosocial tendencies such as generosity, trust, and trustworthiness were positively correlated, constituting a 'cooperativeness syndrome', but the tendency to punish non-cooperative individuals is not part of this syndrome. In a personalised setting, information on the appearance of the interaction partner systematically affected cooperation-related behaviour. Subjects were more cooperative toward interaction partners whose facial photographs were judged 'generous', 'trustworthy', 'not greedy', 'happy', 'attractive', and 'not angry' by a separate panel. However, individuals eliciting more cooperation were not more cooperative themselves in our experiment. Our study shows that a multi-faceted approach can reveal general behavioural tendencies underlying cooperation, but it also uncovers new puzzling features of human cooperation.

Acoustic correlates of perceived personality from Korean utterances in a formal communicative setting

The aim of the present study was to find acoustic correlates of perceived personality from the speech produced in a formal communicative setting-that of Korean customer service employees in particular. This work extended previous research on voice personality impressions to a different sociocultural and linguistic context in which speakers are expected to speak politely in a formal register. To use naturally produced speech rather than read speech, we devised a new method that successfully elicited spontaneous speech from speakers who were role-playing as customer service employees, while controlling for the words and sentence structures they used. We then examined a wide range of acoustic properties in the utterances, including voice quality and global acoustic and segmental properties using Principal Component Analysis. Subjects of the personality rating task listened to the utterances and rated perceived personality in terms of the Big-Five personality traits. While replicating some previous findings, we discovered several acoustic variables that exclusively accounted for the personality judgments of female speakers; a more modal voice quality increased perceived conscientiousness and neuroticism, and less dispersed formants reflecting a larger body size increased the perceived levels of extraversion and openness. These biases in personality perception likely reflect gender and occupation-related stereotypes that exist in South Korea. Our findings can also serve as a basis for developing and evaluating synthetic speech for Voice Assistant applications in future studies.

Chronotype and Time of Day Effects on a Famous Face Recognition Task with Dynamic Stimuli

Chronotype and Time of Day (ToD) can modulate several aspects of cognitive performance. However, there is limited evidence about the effect of these variables on face recognition performance, so the aim of the present study is to investigate this influence. For this, 274 participants (82.5% females; age 18-49 years old, mean = 27.2, SD = 1.82) were shown 20 short videoclips, each gradually morphing from a general identity unfamiliar face to a famous face. Participants should press the spacebar to stop each video as soon as they could identify the famous face, and then provide the name or an unequivocal description of the person. Analysis of response times (RT) showed that evening-types recognised the faces faster than morning-types. Considering different ToD windows, the effect of chronotype was only significant in the 13h-17h and in the 21h-6h time-windows. Altogether, results suggest an advantage of evening-types on famous face recognition using dynamic stimuli with morning-types, being particularly slower during their non-optimal period.

Identifying personal physiological data risks to the Internet of Everything: the case of facial data breach risks

Personal physiological data is the digital representation of physical features that identify individuals in the Internet of Everything environment. Such data includes characteristics of uniqueness, identification, replicability, irreversibility of damage, and relevance of information, and this data can be collected, shared, and used in a wide range of applications. As facial recognition technology has become prevalent and smarter over time, facial data associated with critical personal information poses a potential security and privacy risk of being leaked in the Internet of Everything application platform. However, current research has not identified a systematic and effective method for identifying these risks. Thus, in this study, we adopted the fault tree analysis method to identify risks. Based on the risks identified, we then listed intermediate events and basic events according to the causal logic, and drew a complete fault tree diagram of facial data breaches. The study determined that personal factors, data management and supervision absence are the three intermediate events. Furthermore, the lack of laws and regulations and the immaturity of facial recognition technology are the two major basic events leading to facial data breaches. We anticipate that this study will explain the manageability and traceability of personal physiological data during its lifecycle. In addition, this study contributes to an understanding of what risks physiological data faces in order to inform individuals of how to manage their data carefully and to guide management parties on how to formulate robust policies and regulations that can ensure data security.

Case report: binaural beats music assessment experiment

We recruited subjects with the focus on people who were stressed and needed a break to experience relaxation. The study used inaudible binaural beats (BB) to measure the ability of BB to induce a relaxed state. We found through measuring brain wave activity that in fact BB seem to objectively induce a state of relaxation. We were able to see this across several scores, F3/F4 Alpha Assessment and CZ Theta Beta, calculated from EEG readings, that indicated an increase in positive outlook and a relaxing brain, respectively, and scalp topography maps. Most subjects also showed an improvement in Menlascan measurements of microcirculation or cardiovascular score, although the Menlascan scores and Big Five character assessment results were less conclusive. BB seem to have profound effects on the physiology of subjects and since the beats were not audible, these effects could not be attributed to the placebo effect. These results are encouraging in terms of developing musical products incorporating BB to affect human neural rhythms and corollary states of consciousness and warrant further research with more subjects and different frequencies of BB and different music tracks.

Using deep learning to predict ideology from facial photographs: expressions, beauty, and extra-facial information

Deep learning techniques can use public data such as facial photographs to predict sensitive personal information, but little is known about what information contributes to the predictive success of these techniques. This lack of knowledge limits both the public's ability to protect against revealing unintended information as well as the scientific utility of deep learning results. We combine convolutional neural networks, heat maps, facial expression coding, and classification of identifiable features such as masculinity and attractiveness in our study of political ideology in 3323 Danes. Predictive accuracy from the neural network was 61% in each gender. Model-predicted ideology correlated with aspects of both facial expressions (happiness vs neutrality) and morphology (specifically, attractiveness in females). Heat maps highlighted the informativeness of areas both on and off the face, pointing to methodological refinements and the need for future research to better understand the significance of certain facial areas.

CAS(ME)3: A Third Generation Facial Spontaneous Micro-Expression Database With Depth Information and High Ecological Validity

Micro-expression (ME) is a significant non-verbal communication clue that reveals one person's genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME) 3. The contribution of this article is summarized as follows: (1) CAS(ME) 3 offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME) 3 provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME) 3 elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME) 3 provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information.

Analysis of personality traits' correlation to facial width-to-height ratio (fWHR) and mandibular line angle based on 16 personality factor in Chinese college students

Facial appearance reveals clues about personality. Studies have found that facial width-to-height ratio (fWHR) correlates with some personality traits, and mandibular morphology as a potential facial feature that might have correlation with personality traits. Therefore, a face recognition study was carried out to explore the personality traits' correlation to both fWHR and bilateral mandibular line angles. Specifically, face images of 904 college students in China were collected and measured, with the personality traits evaluated using the 16 Personality Factor Questionnaire. Analyses revealed that the average bilateral mandibular line angle of the male were significantly more extensive than that of the female, while the fWHR of the female was significantly more extensive than that of the male. We found facial features (fWHR and average bilateral mandibular line angle) were correlated with 16PF in the canonical correlation analysis and the loadings of bilateral mandibular line angles were greater than that of fWHR. The fWHR was significantly negatively correlated with the scores of sensitivity and self-reliance in male but none of the factors related to fWHR in female. The bilateral mandibular line angles were significantly negatively correlated with the scores of social boldness in male, and were significantly negatively correlated with the scores of vigilance and apprehension in female. Over all, the correlations between fWHR, average bilateral mandibular line angle and certain 16PF factors in male and female tend to be different, suggesting that such correlations might vary with gender. In the future, mandibular morphology could be selected as a potential indicator in facial perception. The limitations of this study were the participants were limited to 18-30 years of age and the mandibular morphology was not measured with anthropometry, which could be further improved in future studies.

Twenty-first century bioarchaeology: Taking stock and moving forward

This article presents outcomes from a Workshop entitled "Bioarchaeology: Taking Stock and Moving Forward," which was held at Arizona State University (ASU) on March 6-8, 2020. Funded by the National Science Foundation (NSF), the School of Human Evolution and Social Change (ASU), and the Center for Bioarchaeological Research (CBR, ASU), the Workshop's overall goal was to explore reasons why research proposals submitted by bioarchaeologists, both graduate students and established scholars, fared disproportionately poorly within recent NSF Anthropology Program competitions and to offer advice for increasing success. Therefore, this Workshop comprised 43 international scholars and four advanced graduate students with a history of successful grant acquisition, primarily from the United States. Ultimately, we focused on two related aims: (1) best practices for improving research designs and training and (2) evaluating topics of contemporary significance that reverberate through history and beyond as promising trajectories for bioarchaeological research. Among the former were contextual grounding, research question/hypothesis generation, statistical procedures appropriate for small samples and mixed qualitative/quantitative data, the salience of Bayesian methods, and training program content. Topical foci included ethics, social inequality, identity (including intersectionality), climate change, migration, violence, epidemic disease, adaptability/plasticity, the osteological paradox, and the developmental origins of health and disease. Given the profound changes required globally to address decolonization in the 21st century, this concern also entered many formal and informal discussions.

Educational Psychology Analysis Method for Extracting Students' Facial Information Based on Image Big Data

At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students' facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student's academic mood, they can intervene on the students' positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students' positive academic emotions, in order to improve students' positive academic emotions, which will improve students' academic performance and teaching.

Supporting Artificial Social Intelligence With Theory of Mind

In this paper, we discuss the development of artificial theory of mind as foundational to an agent's ability to collaborate with human team members. Agents imbued with artificial social intelligence will require various capabilities to gather the social data needed to inform an artificial theory of mind of their human counterparts. We draw from social signals theorizing and discuss a framework to guide consideration of core features of artificial social intelligence. We discuss how human social intelligence, and the development of theory of mind, can contribute to the development of artificial social intelligence by forming a foundation on which to help agents model, interpret and predict the behaviors and mental states of humans to support human-agent interaction. Artificial social intelligence will need the processing capabilities to perceive, interpret, and generate combinations of social cues to operate within a human-agent team. Artificial Theory of Mind affords a structure by which a socially intelligent agent could be imbued with the ability to model their human counterparts and engage in effective human-agent interaction. Further, modeling Artificial Theory of Mind can be used by an ASI to support transparent communication with humans, improving trust in agents, so that they may better predict future system behavior based on their understanding of and support trust in artificial socially intelligent agents.

Mental states and personality based on real-time physical activity and facial expression recognition

INTRODUCTION

To explore a quick and non-invasive way to measure individual psychological states, this study developed interview-based scales, and multi-modal information was collected from 172 participants.

METHODS

We developed the Interview Psychological Symptom Inventory (IPSI) which eventually retained 53 items with nine main factors. All of them performed well in terms of reliability and validity. We used optimized convolutional neural networks and original detection algorithms for the recognition of individual facial expressions and physical activity based on Russell's circumplex model and the five factor model.

RESULTS

We found that there was a significant correlation between the developed scale and the participants' scores on each factor in the Symptom Checklist-90 (SCL-90) and Big Five Inventory (BFI-2) [r = (-0.257, 0.632), p < 0.01]. Among the multi-modal data, the arousal of facial expressions was significantly correlated with the interval of validity (p < 0.01), valence was significantly correlated with IPSI and SCL-90, and physical activity was significantly correlated with gender, age, and factors of the scales.

DISCUSSION

Our research demonstrates that mental health can be monitored and assessed remotely by collecting and analyzing multimodal data from individuals captured by digital tools.

Facial recognition technology can expose political orientation from naturalistic facial images

Ubiquitous facial recognition technology can expose individuals’ political orientation, as faces of liberals and conservatives consistently differ. A facial recognition algorithm was applied to naturalistic images of 1,085,795 individuals to predict their political orientation by comparing their similarity to faces of liberal and conservative others. Political orientation was correctly classified in 72% of liberal–conservative face pairs, remarkably better than chance (50%), human accuracy (55%), or one afforded by a 100-item personality questionnaire (66%). Accuracy was similar across countries (the U.S., Canada, and the UK), environments (Facebook and dating websites), and when comparing faces across samples. Accuracy remained high (69%) even when controlling for age, gender, and ethnicity. Given the widespread use of facial recognition, our findings have critical implications for the protection of privacy and civil liberties.