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

Recent Trends in Deep Learning Based Personality Detection

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection

AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data

�rm Face image analysis in dynamic sce

Automatic personality analysis using computer vision is a relatively new research topic. It investigates how a machine could automatically identify or synthesize human personality. Utilizing time-based sequence information, numerous attempts have been made to tackle this problem. Various applications can benefit from such a system, including prescreening interviews and personalized agents. In this thesis, we address the challenge of estimating the Big-Five personality traits along with the job candidate screening variable from facial videos. We proposed a novel framework to assist in solving this challenge. This framework is based on two main components: (1) the use of Pyramid Multilevel (PML) to extract raw facial textures at different scales and levels; and (2) the extension of the Covariance Descriptor (COV) to combine several local texture features of the face image, such as Local Binary Patterns (LBP), Local Directional Pattern (LDP), Binarized Statistical Image Features (BSIF), and Local Phase Quantization (LPQ). The video stream features are then represented by merging the face feature vectors, where each face feature vector is formed by concatenating all iii iii the PML-COV feature blocks. These rich low-level feature blocks are obtained by feeding the textures of PML face parts into the COV descriptor. The state-of-the-art approaches are even hand-crafted or based on deep learning. The Deep Learning methods perform better than the hand-crafted descriptors, but they are computationally and experimentally expensive. In this study, we compared five hand-crafted methods against five methods based on deep learning in order to determine the optimal balance between accuracy and computational cost. The obtained results of our PML-COV framework on the ChaLearn LAP APA2016 dataset compared favourably with the state-ofthe-art approaches, including deep learning-based ones. Our future aim is to apply this framework to other similar computer vision problems

Highly Accurate, But Still Discriminatory

The study aims to identify whether algorithmic decision making leads to unfair (i.e., unequal) treatment of certain protected groups in the recruitment context. Firms increasingly implement algorithmic decision making to save costs and increase efficiency. Moreover, algorithmic decision making is considered to be fairer than human decisions due to social prejudices. Recent publications, however, imply that the fairness of algorithmic decision making is not necessarily given. Therefore, to investigate this further, highly accurate algorithms were used to analyze a pre-existing data set of 10,000 video clips of individuals in self-presentation settings. The analysis shows that the under-representation concerning gender and ethnicity in the training data set leads to an unpredictable overestimation and/or underestimation of the likelihood of inviting representatives of these groups to a job interview. Furthermore, algorithms replicate the existing inequalities in the data set. Firms have to be careful when implementing algorithmic video analysis during recruitment as biases occur if the underlying training data set is unbalanced

Putting Your Best Face Forward: The Influence of Facial Cosmetics on Structured Employment Interview Ratings

Employment interviews are ubiquitous in modern selection systems. Although interviews are extremely common, there is evidence that interview ratings are subject to rating errors and biases. For example, previous research has found that higher physical attractiveness of the candidate is linked to increased interview ratings. Physical attractiveness is largely considered to be a fixed characteristic that cannot be controlled, however this may not be entirely true as research has consistently linked women\u27s use of facial cosmetics to increased ratings of physical attractiveness. An experimental three (no cosmetics, low cosmetics, high cosmetics) by three (low performance, intermediate performance, high performance) design was used to examine: a) what amount of facial cosmetics is most beneficial to interview ratings, b) the explanatory mediators of the cosmetics-interview ratings relationship, and c) the influence of interview performance on the cosmetics-interview ratings relationship. Participants included 452 individuals recruited using Amazon\u27s Mechanical Turk. Results indicated that there was not a direct relationship between facial cosmetics use and interview ratings, but facial cosmetics did indirectly affect interview ratings through the mediating variables of physical attractiveness and professional appearance. Ratings of professional appearance were highest in the low cosmetics condition, suggesting that the amount of makeup worn effects perceptions of professional appearance. Contrary to expectations, facial cosmetics did not affect perceived competence, perceived competence did not mediate the relationship between facial cosmetics and interview ratings, and interview performance did not moderate the relationship between facial cosmetics and interview ratings. Overall, the results of this dissertation provide some support for the common advice that it is important for women to wear makeup to job interviews