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

An ongoing review of speech emotion recognition

User emotional status recognition is becoming a key feature in advanced Human Computer Interfaces (HCI). A key source of emotional information is the spoken expression, which may be part of the interaction between the human and the machine. Speech emotion recognition (SER) is a very active area of research that involves the application of current machine learning and neural networks tools. This ongoing review covers recent and classical approaches to SER reported in the literature.This work has been carried out with the support of project PID2020-116346GB-I00 funded by the Spanish MICIN

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

Multimodal Sentiment Sensing and Emotion Recognition Based on Cognitive Computing Using Hidden Markov Model with Extreme Learning Machine

In today's competitive business environment, exponential increase of multimodal content results in a massive amount of shapeless data. Big data that is unstructured has no specific format or organisation and can take any form, including text, audio, photos, and video. Many assumptions and algorithms are generally required to recognize different emotions as per literature survey, and the main focus for emotion recognition is based on single modality, such as voice, facial expression and bio signals. This paper proposed the novel technique in multimodal sentiment sensing with emotion recognition using artificial intelligence technique. Here the audio and visual data has been collected based on social media review and classified using hidden Markov model based extreme learning machine (HMM_ExLM). The features are trained using this method. Simultaneously, these speech emotional traits are suitably maximised. The strategy of splitting areas is employed in the research for expression photographs and various weights are provided to each area to extract information. Speech as well as facial expression data are then merged using decision level fusion and speech properties of each expression in region of face are utilized to categorize. Findings of experiments show that combining features of speech and expression boosts effect greatly when compared to using either speech or expression alone. In terms of accuracy, recall, precision, and optimization level, a parametric comparison was made

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