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

ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results

This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the first round of the competition. The goal of the competition was to automatically evaluate five “apparent” personality traits (the so-called “Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the final phase. Despite the difficulty of the task, the teams made great advances in this round of the challenge

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP

Audio-visual deep learning regression of apparent personality

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Sergio Escalera Guerrero, Cristina Palmero Cantariño i Julio Jacques Junior[en] Personality perception is based on the relationship of the human being with the individuals of his surroundings. This kind of perception allows to obtain conclusions based on the analysis and interpretation of the observable, mainly face expressions, tone of voice and other nonverbal signals, allowing the construction of an apparent personality (or first impression) of people. Apparent personality (or first impressions) are subjective, and subjectivity is an inherent property of perception based exclusively on the point of view of each individual. In this project, we approximate such subjectivity using a multi-modal deep neural network with audiovisual signals as input and a late fusion strategy of handcrafted features, achieving accurate results. The aim of this work is to perform an analysis of the influence of automatic prediction for apparent personality (based on the Big-Five model), of the following characteristics: raw audio, visual information (sequence of face images) and high-level features, including Ekman's universal basic emotions, gender and age. To this end, we have defined different modalities, performing combinations of them and determining how much they contribute to the regression of apparent personality traits. The most remarkable results obtained through the experiments performed are as follows: in all modalities, females have a higher average accuracy than men, except in the modality with only audio; for happy emotion, the best accuracy score is found in the Conscientiousness trait; Extraversion and Conscientiousness traits get the highest accuracy scores in almost all emotions; visual information is the one that most positively influences the results; the combination of high-level features chosen slightly improves the accuracy performance for predictions

Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods

�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

Non-acted multi-view audio-visual dyadic Interactions. Project master thesis: multi-modal local and recurrent non-verbal emotion recognition in dyadic scenarios

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Sergio Escalera Guerrero i Cristina Palmero[en] In particular, this master thesis is focused on the development of baseline emotion recognition system in a dyadic environment using raw and handcraft audio features and cropped faces from the videos. This system is analyzed at frame and utterance level with and without temporal information. For this reason, an exhaustive study of the state-of-the-art on emotion recognition techniques has been conducted, paying particular attention on Deep Learning techniques for emotion recognition. While studying the state-of-the-art from the theoretical point of view, a dataset consisting of videos of sessions of dyadic interactions between individuals in different scenarios has been recorded. Different attributes were captured and labelled from these videos: body pose, hand pose, emotion, age, gender, etc. Once the architectures for emotion recognition have been trained with other dataset, a proof of concept is done with this new database in order to extract conclusions. In addition, this database can help future systems to achieve better results. A large number of experiments with audio and video are performed to create the emotion recognition system. The IEMOCAP database is used to perform the training and evaluation experiments of the emotion recognition system. Once the audio and video are trained separately with two different architectures, a fusion of both methods is done. In this work, the importance of preprocessing data (i.e. face detection, windows analysis length, handcrafted features, etc.) and choosing the correct parameters for the architectures (i.e. network depth, fusion, etc.) has been demonstrated and studied, while some experiments to study the influence of the temporal information are performed using some recurrent models for the spatiotemporal utterance level recognition of emotion. Finally, the conclusions drawn throughout this work are exposed, as well as the possible lines of future work including new systems for emotion recognition and the experiments with the database recorded in this work