Examining the Influence of Personality and Multimodal Behavior on Hireability Impressions

While personality traits have been traditionally modeled as behavioral constructs, we novelly posit job hireability as a personality construct. To this end, we examine correlates among personality and hireability measures on the First Impressions Candidate Screening dataset. Modeling hireability as both a discrete and continuous variable, and the big-five OCEAN personality traits as predictors, we utilize (a) multimodal behavioral cues, and (b) personality trait estimates obtained via these cues for hireability prediction (HP). For each of the text, audio and visual modalities, HP via (b) is found to be more effective than (a). Also, superior results are achieved when hireability is modeled as a continuous rather than a categorical variable. Interestingly, eye and bodily visual cues perform comparably to facial cues for predicting personality and hireability. Explanatory analyses reveal that multimodal behaviors impact personality and hireability impressions: e.g., Conscientiousness impressions are impacted by the use of positive adjectives (verbal behavior) and eye movements (non-verbal behavior), confirming prior observations

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109

Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions

Flickr allows its users to generate galleries of "faves", i.e., pictures that they have tagged as favourite. According to recent studies, the faves are predictive of the personality traits that people attribute to Flickr users. This article investigates the phenomenon and shows that faves allow one to predict whether a Flickr user is perceived to be above median or not with respect to each of the Big-Five Traits (accuracy up to 79\% depending on the trait). The classifier - based on Gaussian Processes with a new kernel designed for this work - allows one to identify the visual characteristics of faves that better account for the prediction outcome

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

Unseen Affective Faces Influence Person Perception Judgments in Schizophrenia.

To demonstrate the influence of unconscious affective processing on consciously processed information among people with and without schizophrenia, we used a continuous flash suppression (CFS) paradigm to examine whether early and rapid processing of affective information influences first impressions of structurally neutral faces. People with and without schizophrenia rated visible neutral faces as more or less trustworthy, warm, and competent when paired with unseen smiling or scowling faces compared to when paired with unseen neutral faces. Yet, people with schizophrenia also exhibited a deficit in explicit affect perception. These findings indicate that early processing of affective information is intact in schizophrenia but the integration of this information with semantic contexts is problematic. Furthermore, people with schizophrenia who were more influenced by smiling faces presented outside awareness reported experiencing more anticipatory pleasure, suggesting that the ability to rapidly process affective information is important for anticipation of future pleasurable events

Automatic Prediction of Impressions in Time and across Varying Context: Personality, Attractiveness and Likeability

© 2010-2012 IEEE. In this paper, we propose a novel multimodal framework for automatically predicting the impressions of extroversion, agreeableness, conscientiousness, neuroticism , openness, attractiveness and likeability continuously in time and across varying situational contexts. Differently from the existing works, we obtain visual-only and audio-only annotations continuously in time for the same set of subjects, for the first time in the literature, and compare them to their audio-visual annotations. We propose a time-continuous prediction approach that learns the temporal relationships rather than treating each time instant separately. Our experiments show that the best prediction results are obtained when regression models are learned from audio-visual annotations and visual cues, and from audio-visual annotations and visual cues combined with audio cues at the decision level. Continuously generated annotations have the potential to provide insight into better understanding which impressions can be formed and predicted more dynamically, varying with situational context, and which ones appear to be more static and stable over time.This research work was supported by the EPSRC MAPTRAITS Project (Grant Ref: EP/K017500/1) and the EPSRC HARPS Project under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref: EP/L00416X/1)

Actively learning a Bayesian matrix fusion model with deep side information

High-dimensional deep neural network representations of images and concepts can be aligned to predict human annotations of diverse stimuli. However, such alignment requires the costly collection of behavioral responses, such that, in practice, the deep-feature spaces are only ever sparsely sampled. Here, we propose an active learning approach to adaptively sampling experimental stimuli to efficiently learn a Bayesian matrix factorization model with deep side information. We observe a significant efficiency gain over a passive baseline. Furthermore, with a sequential batched sampling strategy, the algorithm is applicable not only to small datasets collected from traditional laboratory experiments but also to settings where large-scale crowdsourced data collection is needed to accurately align the high-dimensional deep feature representations derived from pre-trained networks