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

Ensemble deep learning: A review

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions

Emotion recognition: recognition of emotions through voice

Dissertação de mestrado integrado em Informatics EngineeringAs the years go by, the interaction between humans and machines seems to gain more and more importance for many different reasons, whether it's taken into consideration personal or commercial use. On a time where technology is reaching many parts of our lives, it's important to keep thriving for a healthy progress and help not only to improve but also to maintain the benefits that everyone gets from it. This relationship can be tackled through many points, but here the focus will be on the mind. Emotions are still a mystery. The concept itself brings up serious questions because of its complex nature. Till the date, scientists still struggle to understand it, so it's crucial to pave the right path for the growth on technology on the aid of such topic. There is some consensus on a few indicators that provide important insights on mental state, like words used, facial expressions, voice. The context of this work is on the use of voice and, based on the field of Automatic Speech Emotion Recognition, it is proposed a full pipeline of work with a wide scope by resorting to sound capture and signal processing software, to learning and classifying through algorithms belonging on the Semi Supervised Learning paradigm and visualization techniques for interpretation of results. For the classification of the samples,using a semi-supervised approach with Neural Networks represents an important setting to try alleviating the dependency of human labelling of emotions, a task that has proven to be challenging and, in many cases, highly subjective, not to mention expensive. It is intended to rely mostly on empiric results more than theoretical concepts due to the complexity of the human emotions concept and its inherent uncertainty, but never to disregard prior knowledge on the matter.À medida que os anos passam, a interacção entre indivíduos e máquinas tem vindo a ganhar maior importância por várias razões, quer seja para uso pessoal ou comercial. Numa altura onde a tecnologia está a chegar a várias partes das nossas vidas, é importante continuar a perseguir um progresso saudável e ajudar não só a melhorar mas também manter os benefícios que todos recebem. Esta relação pode ser abordada por vários pontos, neste trabalho o foco está na mente. Emoções são um mistério. O próprio conceito levanta questões sobre a sua natureza complexa. Até aos dias de hoje, muitos cientistas debatem-se para a compreender, e é crucial que um caminho apropriado seja criado para o crescimento de tecnologia na ajuda da compreensão deste assunto. Existe algum consenso sobre indicadores que demonstram pistas importantes sobre o estado mental de um sujeito, como palavras, expressões faciais, voz. O conteúdo deste trabalho foca-se na voz e, com base no campo de Automatic Speech Emotion Recognition, é proposto uma sequência de procedimentos diversificados, ao optar por software de captura de som e processamento de sinais, aprendizagem e classificação através de algoritmos de Aprendizagem Semi Supervisionada e técnicas de visualização para interpretar resultados. Para a classificação de amostras, o uso de uma abordagem Semi Supervisionada com redes neuronais representam um procedimentos importante para tentar combater a alta dependência da anotação de amostras de emoções humanas, uma tarefa que se demonstra ser árdua e, em muitos casos, altamente subjectiva, para não dizer cara. A intenção é estabelecer raciocínios baseados em factores experimentais, mais que teóricos, devido à complexidade do conceito de emoções humanas e à sua incerteza associada, mas tendo sempre em conta conhecimento já estabelecido no assunto

End-to-end Lip-reading: A Preliminary Study

Deep lip-reading is the combination of the domains of computer vision and natural language processing. It uses deep neural networks to extract speech from silent videos. Most works in lip-reading use a multi staged training approach due to the complex nature of the task. A single stage, end-to-end, unified training approach, which is an ideal of machine learning, is also the goal in lip-reading. However, pure end-to-end systems have not yet been able to perform as good as non-end-to-end systems. Some exceptions to this are the very recent Temporal Convolutional Network (TCN) based architectures. This work lays out preliminary study of deep lip-reading, with a special focus on various end-to-end approaches. The research aims to test whether a purely end-to-end approach is justifiable for a task as complex as deep lip-reading. To achieve this, the meaning of pure end-to-end is first defined and several lip-reading systems that follow the definition are analysed. The system that most closely matches the definition is then adapted for pure end-to-end experiments. Four main contributions have been made: i) An analysis of 9 different end-to-end deep lip-reading systems, ii) Creation and public release of a pipeline1 to adapt sentence level Lipreading Sentences in the Wild 3 (LRS3) dataset into word level, iii) Pure end-to-end training of a TCN based network and evaluation on LRS3 word-level dataset as a proof of concept, iv) a public online portal2 to analyse visemes and experiment live end-to-end lip-reading inference. The study is able to verify that pure end-to-end is a sensible approach and an achievable goal for deep machine lip-reading