Big five personality traits questionnaire pdf

People who are high in openness tend to enjoy the arts and seek out unusual, complex forms of self-expression. People who are low in openness are often suspicious of the arts and prefer to focus on more practical pursuits.

Because you are high in Openness, you probably consider yourself to be a creative, imaginative person. You are interested in intellectual development and artistic expression. You are adventurous and unconventional. High Openness scorers are more likely to be politically liberal and to participate in artistic and cultural activities in their leisure time. They tend to be drawn to artistic and scientific careers.

High Openness scorers are also more likely to have a high IQ. How can you use your Openness to your advantage? Upgrade to your premium report and Shares find out! High scorers are organized and determined, and are able to forego immediate gratification for the sake of long-term achievement. Low scorers are impulsive and easily sidetracked.

The concept of Conscientiousness focuses on a dilemma we all face: shall I do what feels good now, or instead do what is less fun but will pay off in the future? Some people are more likely to choose fun in the moment, and thus are low in Conscientiousness.

Others are more likely to work doggedly toward their goals, and thus are high in this trait. Because you are high in Conscientiousness, you are probably orderly and organized. You probably consider yourself very reliable and responsible. You do not mind hard work and are persistent in pursuing your goals. People high in Conscientiousness are more likely to be successful in their careers and less likely to develop addictions of all kinds.

They have high levels of self-control and are good at resisting impulses. They usually have neat, organized homes and orderly, well-planned lives. Extraverts engage actively with others to earn friendship, admiration, power, status, excitement, and romance. Introverts, on the other hand, conserve their energy, and do not work as hard to earn these social rewards. Shares Extraversion seems to be related to the emotional payoff that a person gets from achieving a goal.

While everyone experiences victories in life, it seems that extroverts are especially thrilled by these victories, especially when they earn the attention of others. Getting a promotion, finding a new romance, or winning an award are all likely to bring an extrovert great joy. Introverts tend to be more content with simple, quiet lives, and rarely seek attention from others.

Because you are high in Extraversion, you probably appear to be friendly and gregarious to other people. You show enthusiasm easily and are generally energetic. You are interested in having adventures and achieving many things in your life. People who are high in extraversion are more likely to have many friends and an active social life.

They tend to work hard to achieve power and prestige and get a special thrill from going after rewards such as money, status, or attention from others. People who are high in Agreeableness experience a great deal of empathy and tend to get pleasure out of serving and taking care of others. They are usually trusting and forgiving. People who are low in Agreeableness tend to experience less empathy and put their own concerns ahead of others.

Low scorers are often described as hostile, competitive, and antagonistic. They tend to have more conflictual relationships and often fall out with people. Because you are high in agreeableness, you probably think of yourself as a kind, compassionate person. You sympathize easily with others and want to get along with people. Agreeableness is a good predictor of the quality of relationships: people high in the trait are more likely to keep friends and less likely to have disputes with people.

They are more likely to think of other people kindly and to be forgiving of faults or slights. To find out how being Agreeable affects your relationships, upgrade to your premium report. While everyone experiences these emotions from time to time, some people are more prone to them than others. This trait can be thought of as an alarm system. People experience negative emotions as a sign that something is wrong in the world.

You may be in danger, so you feel fear. Or you may have done something morally wrong, so you feel guilty. However, not everyone has the same reaction to a given situation. High Neuroticism scorers are more likely to react to a situation with fear, anger, sadness, and the like. Low Neuroticism scorers are more likely to brush off their misfortune and move on. Because you are low in Neuroticism, you are less likely than other people to experience negative emotions like fear or sadness.

You are probably optimistic, carefree, and self-confident. You Shares rarely worry about how things will turn out. Low Neuroticism scorers are less likely to get divorced or to suffer mental illness. They tend to handle stress well and take unfortunate events in stride. Major stressors like losing a job or getting a divorce are less likely to cause depression or anxiety in people who have low levels of Neuroticism.

In general, low Neuroticism scorers report solid self-esteem and a positive outlook on life. Your Personality Patterns Your personality traits interact to create unique patterns of thought and behavior. In this section, you'll learn how your traits work together to drive the way you interact with the world. To describe your personality patterns, we use a circular graph called a circumplex.

Given that prenatal and postnatal sex hormone levels do influence behaviour, facial features may correlate with hormonally driven personality characteristics, such as aggressiveness 33 , competitiveness, and dominance, at least for men 34 , Thus, in addition to genes, the associations of facial features with behavioural tendencies may also be explained by androgens and potentially other hormones affecting both face and behaviour.

Fourth and finally, some personality traits are associated with habitual patterns of emotionally expressive behaviour. Existing studies have revealed the links between objective facial picture cues and general personality traits based on the Five-Factor Model or the Big Five BF model of personality However, a quick glance at the sizes of the effects found in these studies summarized in Table 1 reveals much controversy.

The results appear to be inconsistent across studies and hardly replicable These inconsistencies may result from the use of small samples of stimulus faces, as well as from the vast differences in methodologies. Stronger effect sizes are typically found in studies using composite facial images derived from groups of individuals with high and low scores on each of the Big Five dimensions 6 , 7 , 8.

Naturally, the task of identifying traits using artificial images comprised of contrasting pairs with all other individual features eliminated or held constant appears to be relatively easy. This is in contrast to realistic situations, where faces of individuals reflect a full range of continuous personality characteristics embedded in a variety of individual facial features.

Studies relying on photographic images of individual faces, either artificially manipulated 2 , 42 or realistic, tend to yield more modest effects. It appears that studies using realistic photographs made in controlled conditions neutral expression, looking straight at the camera, consistent posture, lighting, and distance to the camera, no glasses, no jewellery, no make-up, etc.

Unfortunately, differences in the methodologies make it hard to hypothesize whether the diversity of these findings is explained by variance in image quality, image background, or the prediction models used. Research into the links between facial picture cues and personality traits faces several challenges. First, the number of specific facial features is very large, and some of them are hard to quantify.

Second, the effects of isolated facial features are generally weak and only become statistically noticeable in large samples.

Third, the associations between objective facial features and personality traits might be interactive and nonlinear. Finally, studies using real-life photographs confront an additional challenge in that the very characteristics of the images e. The task of isolating the contribution of each variable out of the multitude of these individual variables appears to be hardly feasible.

Instead, recent studies in the field have tended to rely on a holistic approach, investigating the subjective perception of personality based on integral facial images. This approach is supported by studies of human face perception, showing that faces are perceived and encoded in a holistic manner by the human brain 43 , 44 , 45 , Existing evidence suggests that various social judgements might be based on a common visual representational system involving the holistic processing of visual information 51 , Thus, even though the associations between isolated facial features and personality characteristics sought by ancient physiognomists have emerged to be weak, contradictory or even non-existent, the holistic approach to understanding the face-personality links appears to be more promising.

An additional challenge faced by studies seeking to reveal the face-personality links is constituted by the inconsistency of the evaluations of personality traits by human raters.

As a result, a fairly large number of human raters is required to obtain reliable estimates of personality traits for each photograph. In contrast, recent attempts at using machine learning algorithms have suggested that artificial intelligence may outperform individual human raters. For instance, S. Hu and colleagues 40 used the composite partial least squares component approach to analyse dense 3D facial images obtained in controlled conditions and found significant associations with personality traits stronger for men than for women.

A similar approach can be implemented using advanced machine learning algorithms, such as artificial neural networks ANNs , which can extract and process significant features in a holistic manner. The recent applications of ANNs to the analysis of human faces, body postures, and behaviours with the purpose of inferring apparent personality traits 53 , 54 indicate that this approach leads to a higher accuracy of prediction compared to individual human raters.

The main difficulty of the ANN approach is the need for large labelled training datasets that are difficult to obtain in laboratory settings. However, ANNs do not require high-quality photographs taken in controlled conditions and can potentially be trained using real-life photographs provided that the dataset is large enough.

The purpose of the current study was to investigate the associations of facial picture cues with self-reported Big Five personality traits by training a cascade of ANNs to predict personality traits from static facial images. The general hypothesis is that a real-life photograph contains cues about personality that can be extracted using machine learning.

Due to the vast diversity of findings concerning the prediction accuracy of different traits across previous studies, we did not set a priori hypotheses about differences in prediction accuracy across traits. We used data from the test dataset containing predicted scores for 3, images associated with 1, individuals. To determine whether the variance in the predicted scores was associated with differences across images or across individuals, we calculated the intraclass correlation coefficients ICCs presented in Table 2.

We derived the individual scores used in all subsequent analyses as the simple averages of the predicted scores for all images provided by each participant. The correlation coefficients between the self-report test scores and the scores predicted by the ANN ranged from 0.

The associations were strongest for conscientiousness and weakest for openness. Extraversion and neuroticism were significantly better predicted for women than for men based on the z test.

For men, conscientiousness was predicted more accurately than the other four traits the differences among the latter were not statistically significant. For women, conscientiousness was predicted more accurately, and openness was predicted less accurately compared to the three other traits. The mean absolute error MAE of prediction ranged between 0. We did not find any associations between the number of photographs and prediction error. The structure of the correlations between the scales was generally similar for the observed test scores and the predicted values, but some coefficients differed significantly based on the z test see Table 3.

Most notably, predicted openness was more strongly associated with conscientiousness negatively and extraversion positively , whereas its association with agreeableness was negative rather than positive. The associations of predicted agreeableness with conscientiousness and neuroticism were stronger than those between the respective observed scores. In women, predicted neuroticism demonstrated a stronger inverse association with conscientiousness and a stronger positive association with openness.

In men, predicted neuroticism was less strongly associated with extraversion than its observed counterpart. To illustrate the findings, we created composite images using Abrosoft FantaMorph 5 by averaging the uploaded images across contrast groups of individuals with the highest and the lowest test scores on each trait.

The resulting morphed images in which individual features are eliminated are presented in Fig. Composite facial images morphed across contrast groups of individuals for each Big Five trait. This study presents new evidence confirming that human personality is related to individual facial appearance. We expected that machine learning in our case, artificial neural networks could reveal multidimensional personality profiles based on static morphological facial features.

We circumvented the reliability limitations of human raters by developing a neural network and training it on a large dataset labelled with self-reported Big Five traits. We expected that personality traits would be reflected in the whole facial image rather than in its isolated features. Based on this expectation, we developed a novel two-tier machine learning algorithm to encode the invariant facial features as a vector in a dimensional space that was used to predict the BF traits by means of a multilayer perceptron.

Although studies using real-life photographs do not require strict experimental conditions, we had to undertake a series of additional organizational and technological steps to ensure consistent facial image characteristics and quality.

Our results demonstrate that real-life photographs taken in uncontrolled conditions can be used to predict personality traits using complex computer vision algorithms. This finding is in contrast to previous studies that mostly relied on high-quality facial images taken in controlled settings. The accuracy of prediction that we obtained exceeds that in the findings of prior studies that used realistic individual photographs taken in uncontrolled conditions e.

The advantage of our methodology is that it is relatively simple e. In the present study, conscientiousness emerged to be more easily recognizable than the other four traits, which is consistent with some of the existing findings 7 , The weaker effects for extraversion and neuroticism found in our sample may be because these traits are associated with positive and negative emotional experiences, whereas we only aimed to use images with neutral or close to neutral emotional expressions.

Finally, this appears to be the first study to achieve a significant prediction of openness to experience. Predictions of personality based on female faces appeared to be more reliable than those for male faces in our sample, in contrast to some previous studies The BF factors are known to be non-orthogonal, and we paid attention to their intercorrelations in our study 56 , Various models have attempted to explain the BF using higher-order dimensions, such as stability and plasticity 58 or a single general factor of personality GFP We discovered that the intercorrelations of predicted factors tend to be stronger than the intercorrelations of self-report questionnaire scales used to train the model.

This finding suggests a potential biological basis of GFP. However, the stronger intercorrelations of the predicted scores can be explained by consistent differences in picture quality just as the correlations between the self-report scales can be explained by social desirability effects and other varieties of response bias Clearly, additional research is needed to understand the context of this finding.

We believe that the present study, which did not involve any subjective human raters, constitutes solid evidence that all the Big Five traits are associated with facial cues that can be extracted using machine learning algorithms. However, despite having taken reasonable organizational and technical steps to exclude the potential confounds and focus on static facial features, we are still unable to claim that morphological features of the face explain all the personality-related image variance captured by the ANNs.

Rather, we propose to see facial photographs taken by subjects themselves as complex behavioural acts that can be evaluated holistically and that may contain various other subtle personality cues in addition to static facial features.

The effect sizes we observed are comparable with the meta-analytic estimates of correlations between self-reported and observer ratings of personality traits: the associations range from 0. Thus, an artificial neural network relying on static facial images outperforms an average human rater who meets the target in person without any prior acquaintance. Given that partner personality and match between two personalities predict friendship formation 63 , long-term relationship satisfaction 64 , and the outcomes of dyadic interaction in unstructured settings 65 , the aid of artificial intelligence in making partner choices could help individuals to achieve more satisfying interaction outcomes.

There are a vast number of potential applications to be explored. The recognition of personality from real-life photos can be applied in a wide range of scenarios, complementing the traditional approaches to personality assessment in settings where speed is more important than accuracy.

Applications may include suggesting best-fitting products or services to customers, proposing to individuals a best match in dyadic interaction settings such as business negotiations, online teaching, etc. Given that the practical value of any selection method is proportional to the number of decisions made and the size and variability of the pool of potential choices 66 , we believe that the applied potential of this technology can be easily revealed at a large scale, given its speed and low cost.

Because the reliability and validity of self-report personality measures is not perfect, prediction could be further improved by supplementing these measures with peer ratings and objective behavioural indicators of personality traits. The fact that conscientiousness was predicted better than the other traits for both men and women emerges as an interesting finding.

From an evolutionary perspective, one would expect the traits most relevant for cooperation conscientiousness and agreeableness and social interaction certain facets of extraversion and neuroticism, such as sociability, dominance, or hostility to be reflected more readily in the human face.

The results are generally in line with this idea, but they need to be replicated and extended by incorporating trait facets in future studies to provide support for this hypothesis. Finally, although we tried to control the potential sources of confounds and errors by instructing the participants and by screening the photographs based on angles, facial expressions, makeup, etc.

First, the real-life photographs we used could still carry a variety of subtle cues, such as makeup, angle, light facial expressions, and information related to all the other choices people make when they take and share their own photographs.

These additional cues could say something about their personality, and the effects of all these variables are inseparable from those of static facial features, making it hard to draw any fundamental conclusions from the findings.

However, studies using real-life photographs may have higher ecological validity compared to laboratory studies; our results are more likely to generalize to real-life situations where users of various services are asked to share self-pictures of their choice.

Another limitation pertains to a geographically bounded sample of individuals; our participants were mostly Caucasian and represented one cultural and age group Russian-speaking adults. Future studies could replicate the effects using populations representing a more diverse variety of ethnic, cultural, and age groups.

Studies relying on other sources of personality data e. The study was carried out in the Russian language. The participants were anonymous volunteers recruited through social network advertisements. They did not receive any financial remuneration but were provided with a free report on their Big Five personality traits. The data were collected online using a dedicated research website and a mobile application. The participants provided their informed consent, completed the questionnaires, reported their age and gender and were asked to upload their photographs.

They were instructed to take or upload several photographs of their face looking directly at the camera with enough lighting, a neutral facial expression and no other people in the picture and without makeup.

The initial sample included 25, participants who completed the questionnaire and uploaded a total of 77, photographs. The final combined dataset comprised 12, valid questionnaires and 31, associated photographs after the data screening procedures below. The participants ranged in age from 18 to 60 The validation dataset included the responses of men who provided facial images and women who provided images.

Due to the sexually dimorphic nature of facial features and certain personality traits particularly extraversion 1 , 67 , 68 , all the predictive models were trained and validated separately for male and female faces. The research was carried out in accordance with the Declaration of Helsinki.

The morphed group average images presented in the paper do not allow the identification of individuals. No information or images that could lead to the identification of study participants have been published. To detect systematic careless responses, we used the modal response category count, maximum longstring maximum number of identical responses given in sequence by participant , and inter-item standard deviation for each questionnaire.

To detect random responses, we calculated the following person-fit indices: the person-total response profile correlation, the consistency of response profiles for the first and the second half of the questionnaire, the consistency of response profiles obtained based on equivalent groups of items, the number of polytomous Guttman errors, and the intraclass correlation of item responses within facets.

For each distribution, we generated a training dataset and a test dataset, each comprised of 1, simulated responses and 1, real responses drawn randomly from the sample.

Next, we ran a logistic regression model using simulated vs real responses as the outcome variable and chose an optimal cutoff point to minimize the misclassification error using the R package optcutoff.

The sensitivity value was 0. We used a modified Russian version of the 5PFQ questionnaire 70 , which is a item measure of the Big Five model, with 15 items per trait grouped into five three-item facets.

To confirm the structural validity of the questionnaire, we tested an exploratory structural equation ESEM model with target rotation in Mplus 8. The items were treated as ordered categorical variables using the WLSMV estimator, and facet variance was modelled by introducing correlated uniqueness values for the items comprising each facet. To assess the reliability of the scales, we calculated two internal consistency indices, namely, robust omega using the R package coefficientalpha and algebraic greatest lower bound GLB reliability using the R package psych 71 see Table 4.

The images photographs and video frames were subjected to a three-step screening procedure aimed at removing fake and low-quality images. First, images with no human faces or with more than one human face were detected by our computer vision CV algorithms and automatically removed.

The model showed a Third, we performed a manual moderation of the remaining images to remove images with partially covered faces, those that were evidently photoshopped or any other fake images not detected by CV. The images retained for subsequent processing were converted to single-channel 8-bit greyscale format using the OpenCV framework opencv.

Head position pitch, yaw, roll was measured using our own dedicated neural network multilayer perceptron trained on a sample of 8 images labelled by our team. The mean absolute error achieved on the test sample of images was 2. Next, we assessed emotional neutrality using the Microsoft Cognitive Services API on the Azure platform score range: 0 to 1 and used 0. Finally, we applied the face and eye detection, alignment, resize, and crop functions available within the Dlib dlib.

Images with low resolution that contained less than 60 pixels between the eyes, were excluded in the process. The final photoset comprised 41, images.

After the screened questionnaire responses and images were joined, we obtained a set of 12, valid Big Five questionnaires associated with 31, validated images an average of 2.

First, we developed a computer vision neural network NNCV aiming to determine the invariant features of static facial images that distinguish one face from another but remain constant across different images of the same person. We aimed to choose a neural network architecture with a good feature space and resource-efficient learning, considering the limited hardware available to our research team. We chose a residual network architecture based on ResNet 73 see Fig.

This type of neural network was originally developed for image classification. As a measure of success, we calculated the Euclidean distance between the vectors generated from different images. The training was conducted on a server equipped with four NVidia Titan accelerators. The trained neural network was validated on a dataset of 40, images belonging to people, which was an out-of-sample part of the original dataset.

The Euclidean distance threshold for the vectors belonging to the same person was 0. Finally, we trained a personality diagnostics neural network NNPD , which was implemented as a multilayer perceptron see Fig. The network was trained using the same hardware, and the training process took 9 days. The whole process was performed for male and female faces separately. The set of photographs is not made available because we did not solicit the consent of the study participants to publish the individual photographs.

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