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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
Interpretable Convolutional Neural Networks
This paper proposes a method to modify traditional convolutional neural
networks (CNNs) into interpretable CNNs, in order to clarify knowledge
representations in high conv-layers of CNNs. In an interpretable CNN, each
filter in a high conv-layer represents a certain object part. We do not need
any annotations of object parts or textures to supervise the learning process.
Instead, the interpretable CNN automatically assigns each filter in a high
conv-layer with an object part during the learning process. Our method can be
applied to different types of CNNs with different structures. The clear
knowledge representation in an interpretable CNN can help people understand the
logics inside a CNN, i.e., based on which patterns the CNN makes the decision.
Experiments showed that filters in an interpretable CNN were more semantically
meaningful than those in traditional CNNs.Comment: In this version, we release the website of the code. Compared to the
previous version, we have corrected all values of location instability in
Table 3--6 by dividing the values by sqrt(2), i.e., a=a/sqrt(2). Such
revisions do NOT decrease the significance of the superior performance of our
method, because we make the same correction to location-instability values of
all baseline
Being the center of attention: A Person-Context CNN framework for Personality Recognition
This paper proposes a novel study on personality recognition using video data
from different scenarios. Our goal is to jointly model nonverbal behavioral
cues with contextual information for a robust, multi-scenario, personality
recognition system. Therefore, we build a novel multi-stream Convolutional
Neural Network framework (CNN), which considers multiple sources of
information. From a given scenario, we extract spatio-temporal motion
descriptors from every individual in the scene, spatio-temporal motion
descriptors encoding social group dynamics, and proxemics descriptors to encode
the interaction with the surrounding context. All the proposed descriptors are
mapped to the same feature space facilitating the overall learning effort.
Experiments on two public datasets demonstrate the effectiveness of jointly
modeling the mutual Person-Context information, outperforming the state-of-the
art-results for personality recognition in two different scenarios. Lastly, we
present CNN class activation maps for each personality trait, shedding light on
behavioral patterns linked with personality attributes
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
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