Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data

There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM Multimedia Conference (MM'18

Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis

With electroencephalography (EEG) brain waves alone, it is full-scale phenomena in the field of computer-brain interface DNN, CNN, and SVM have improved detection and prediction accuracy in a number of researches during the last several years. But when it comes to recognizing global reliance, both deep learning and SVM have obvious limits. Pre-processing, extraction capabilities, and network design are the most common techniques used in deep learning models today, yet they are still unable to produce reliable results in noisy and sparse datasets. Any dataset, no matter how little or large, may suffer from poor SVM performance due to overlapping target instructions and boundaries. There are many different sorts of emotions that may be classified using the particular approach employed in this research. In order to get a whole picture of a person's mental state, it is best to use a "Min of mean” proposed technique. After comparison to the referential mean, a feeling is divided into one of four emotional quadrants. The MIN Max range is used to further split the emotion into 12 subcategories based on the amount of arousal. The proposed set of rules performed better than existing methods. Research on multi-class emotion reputation has shown that, compared to more recent studies, the proposed technique may be rather strong. It is possible to analyze a person's mental health by using the emotional spectrum, which has an accuracy rate of above 90%

A Short Survey on Deep Learning for Multimodal Integration: Applications, Future Perspectives and Challenges

Deep learning has achieved state-of-the-art performances in several research applications nowadays: from computer vision to bioinformatics, from object detection to image generation. In the context of such newly developed deep-learning approaches, we can define the concept of multimodality. The objective of this research field is to implement methodologies which can use several modalities as input features to perform predictions. In this, there is a strong analogy with respect to what happens with human cognition, since we rely on several different senses to make decisions. In this article, we present a short survey on multimodal integration using deep-learning methods. In a first instance, we comprehensively review the concept of multimodality, describing it from a two-dimensional perspective. First, we provide, in fact, a taxonomical description of the multimodality concept. Secondly, we define the second multimodality dimension as the one describing the fusion approaches in multimodal deep learning. Eventually, we describe four applications of multimodal deep learning to the following fields of research: speech recognition, sentiment analysis, forensic applications and image processing

An Analysis of Facial Expression Recognition Techniques

In present era of technology , we need applications which could be easy to use and are user-friendly , that even people with specific disabilities use them easily. Facial Expression Recognition has vital role and challenges in communities of computer vision, pattern recognition which provide much more attention due to potential application in many areas such as human machine interaction, surveillance , robotics , driver safety, non- verbal communication, entertainment, health- care and psychology study. Facial Expression Recognition has major importance ration in face recognition for significant image applications understanding and analysis. There are many algorithms have been implemented on different static (uniform background, identical poses, similar illuminations ) and dynamic (position variation, partial occlusion orientation, varying lighting )conditions. In general way face expression recognition consist of three main steps first is face detection then feature Extraction and at last classification. In this survey paper we discussed different types of facial expression recognition techniques and various methods which is used by them and their performance measures

Emotion Recognition Based on Deep Learning with Autoencoder

Facial expression is one way of expressing emotions. Face emotion recognition is one of the important and major fields of research in the field of computer vision. Face emotion recognition is still one of the unique and challenging areas of research because it can be combined with various methods, one of which is deep learning. Deep learning is popular in the research area because it has the advantage of processing large amounts of data and automatically learning features on raw data, such as face emotion. Deep learning consists of several methods, one of which is the convolutional neural network method that will be used in this study. This study also uses the convolutional auto-encoder (CAE) method to explore the advantages that can arise compared to previous studies. CAE has advantages for image reconstruction and image de-noising, but we will explore CAE to do classification with CNN. Input data will be processed using CAE, then proceed with the classification process using CNN. Face emotion recognition model will use the Karolinska Directed Emotional Faces (KDEF) dataset of 4900 images divided into 2 groups, 80% for training and 20% for testing. The KDEF data consists of 7 emotional models with 5 angles from 70 different people. The test results showed an accuracy of 81.77%

MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data

Desire is a set of human aspirations and wishes that comprise verbal and cognitive aspects that drive human feelings and behaviors, distinguishing humans from other animals. Understanding human desire has the potential to be one of the most fascinating and challenging research domains. It is tightly coupled with sentiment analysis and emotion recognition tasks. It is beneficial for increasing human-computer interactions, recognizing human emotional intelligence, understanding interpersonal relationships, and making decisions. However, understanding human desire is challenging and under-explored because ways of eliciting desire might be different among humans. The task gets more difficult due to the diverse cultures, countries, and languages. Prior studies overlooked the use of image-text pairwise feature representation, which is crucial for the task of human desire understanding. In this research, we have proposed a unified multimodal transformer-based framework with image-text pair settings to identify human desire, sentiment, and emotion. The core of our proposed method lies in the encoder module, which is built using two state-of-the-art multimodal transformer models. These models allow us to extract diverse features. To effectively extract visual and contextualized embedding features from social media image and text pairs, we conducted joint fine-tuning of two pre-trained multimodal transformer models: Vision-and-Language Transformer (ViLT) and Vision-and-Augmented-Language Transformer (VAuLT). Subsequently, we use an early fusion strategy on these embedding features to obtain combined diverse feature representations of the image-text pair. This consolidation incorporates diverse information about this task, enabling us to robustly perceive the context and image pair from multiple perspectives.Comment: 28 pages, 4 figure

Machine learning and deep learning for emotion recognition

Ús de diferents tècniques de deep learning per al reconeixement d'emocions a partir d'imatges i videos. Les diferents tècniques s'apliquen, es valoren i comparen amb l'objectiu de fer-les servir conjuntament en una aplicació final.Outgoin