Authors: Rewati Saha, Sachin Meshram
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As we know we are living in the era of digital world where everything is based on data analysis, now a days after covid it’s really difficult to do the analysis on real world, so there is need of an algorithm which is able to do the analysis on virtual world, suppose there is any application which is able to identify the user feedback based on there emotion, so there is need of a novel algorithm which is work on the concept of the emotion analysis, in this paper basically we did the study about the previous existing work on emotion analysis and try to find out the research gaps and there future scope.
In some cases speech or high level scene context can also be useful to infer emotion. Most of the time there is a considerable overlap between emotion classes making it a challenging classication task. In this paper we present a deep learning based approach to modeling different input modalities and to combining them in order to infer emotion labels from a given video sequence. The Emotion recognition in the wild (EmotiW 2015) challenge  is an extension of a similar challenge held in 2014 . The task is to predict one of seven emotion labels: angry, disgust, fear, happy, sad, surprise and neutral. The dataset used in the challenge is the Acted Facial Expressions in the Wild (AFEW) 5.0 dataset, which contains short video clips extracted from Hollywood movies. The video clips present emotions with a high degree of variation, e.g. actor identity, age, pose and lighting conditions. The dataset contains 723 videos for training, 383 for validation and 539 test clips. Traditional approaches to emotion recognition were based on hand-engineered features [17, 28]. With the availability of big datasets, deep learning has emerged as a general approach to machine learning yielding state-of-the-art results in many computer vision and natural language processing tasks [22, 19]. The basic principle of deep learning is to learn hierarchical representations of input data such that the learned representations improve classi cation performance. The primary contribution of this work is to model the spatio-temporal evolution of facial expressions of a person in a video using a Recurrent Neural Network (RNN) combined with a Convolutional Neural Network (CNN) in an underlying CNN-RNN architecture. In addition to this, we also employed an Autoencoder based activity recognition pipeline for modelling user activity and a simple Support Vector Machine (SVM) based approach over energy and spectral features for audio. We also present a neural network-based feature level fusion technique to combine di erent modalities for the nal emotion prediction for a short video clip. Previous work [18, 25] has achieved state-of-the-art results in the emotion recognition challenge using deep learning techniques which includes our work that won the 2013 Emotion challenge. In contrast to [18, 16], which use an averaging-based aggregation method for visual features in video, here we employ an RNN to model the temporal evolution of facial features in video. We also explore feature level fusion of our modality-specific models and show that this increases performance. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and also helps in understanding behavioral aspect of human. Emotion play pivotal role during communication. Emotion recognition is carried out in diverse way, it may be verbal or non-verbal .Voice (Audible) is verbal form of communication & Facial expression, action, body postures and gesture is non-verbal form of communication.  While communicating only 7% effect of message is contributes by verbal part as a whole, 38% by vocal part and 55% effect of the speaker’s message is contributed by facial expression. For that reason automated & real time facial expression would play important role in human and machine interaction. Facial expression recognition would be useful from human facilities to clinical practices. Analysis of facial expression plays fundamental roles for applications which are based on emotion recognition like Human Computer Interaction (HCI), Social Robot, Animation, Alert System & Pain monitoring for patients. This paper presents brief introduction of facial expression in section Facial expression presents key mechanism to describe human emotion. From starting to end of the day human changes plenty of emotions, it may be because of their mental or physical circumstances.
Although humans are filled with various emotions, modern psychology defines six basic facial expressions: Happiness, Sadness, Surprise, Fear, Disgust, and Anger as universal emotions . Facial muscles movements help to identify human emotions. Basic facial features are eyebrow, mouth, nose & eyes. In AI, design acknowledgment and in picture handling, highlight extraction begins from an underlying arrangement of estimated information and manufactures inferred values (highlights) planned to be enlightening and non-excess, encouraging the consequent learning and speculation steps, and now and again prompting better human elucidations. Highlight extraction is identified with dimensionality decrease. At the point when the information to a calculation is too enormous to possibly be prepared and it is suspected to be excess (for example a similar estimation in the two feet and meters, or the tedium of pictures displayed as pixels), at that point it tends to be changed into a decreased arrangement of highlights (additionally named an element vector). Deciding a subset of the underlying highlights is called include selection. The chose highlights are relied upon to contain the pertinent data from the info information, so the ideal errand can be performed by utilizing this decreased portrayal rather than the total introductory information. Highlight extraction includes decreasing the quantity of assets required to portray a huge arrangement of information. When performing examination of complex information one of the serious issues comes from the quantity of factors included. Examination with countless factors for the most part requires a lot of memory and calculation control, likewise it might make a characterization calculation overfit to preparing tests and sum up inadequately to new examples. Highlight extraction is a general term for techniques for developing mixes of the factors to get around these issues while as yet portraying the information with adequate precision. Many MACHINE LEARNING experts accept that appropriately enhanced component extraction is the way to successful model construction.The remainder of the paper is sorted out as follows. Vital writing overview related past research on leaf deficiency identification are given in II recognition based past work are given in segment ii though area III portrays explore issue and future degree philosophy and IMPLEMENTATION FOR THE PREVIOUS EXISTING APPROACHES. IV portrays philosophy and IMPLEMENTATION FOR THE PREVIOUS EXISTING APPROACHES. Trial results and its examination are given in area V. At long last, area VI closes the paper.
II. LITRECTURE REVIEW
Encoding and understanding feelings is especially significant in instructive settings [3,31]. While eye to eye instruction with a fit, taught, and sympathetic instructor is ideal, it is additionally not generally imaginable. Individuals have been checking out instructing without educators since the time the innovation of books and with the new advances in innovation, for instance by utilizing recreations [43,66]. We have additionally seen huge advances in distance learning stages and frameworks [22,52]. In any case, while mechanization brings many benefits, like arriving at a wide populace of students or being accessible at areas where eye to eye training may not be imaginable, it additionally brings new difficulties [2,9,50,61]. One of them is the normalized look-and-feel of the course. One design doesn't fit all students, the speed of the conveyance ought to be dealt with, the assignments ought to shift contingent upon the level of the student, and the substance ought to be additionally aligned to the singular necessities of students. Full of feeling Agents: Some of these difficulties have been tended to by intuitive educational specialists that have been found powerful in improving distance learning [6,40,47,57]. Among them, vivified instructive specialists assume a significant part [12,39], in light of the fact that they can be effectively controlled and their conduct can be characterized by strategies regularly utilized in PC liveliness, for instance by giving satisfactory motions . Educational specialists with passionate capacities can upgrade associations between the student and the PC and can further develop learning as shown by Kim et al. . A few frameworks have been executed, for instance Lisetti and Nasoz  joined look and physiological signs to perceive a students feelings. DMello and Graesser  presented AutoTutor and they shown that students show an assortment of feelings during learning and they additionally shown that AutoTutor can be intended to recognize feelings and react to them. A virtual specialist SimSensei  takes part in meetings to evoke practices that can be consequently estimated and examined. It utilizes a multimodal detecting framework that catches an assortment of signs that survey the clients full of feeling state, just as to illuminate the specialist to give criticism. The control of the specialists emotional states essentially impacts learning  and affects student self-adequacy . Be that as it may, a powerful academic specialist needs to react to students feelings that should be first identified. The correspondence ought to be founded on genuine contribution from the student, academic specialists ought to be sympathetic [11,30] and they ought to give passionate associations the student . Different method for feeling discovery have been proposed, for example, utilizing eye-tracker , estimating internal heat level , utilizing visual setting , or skin conductivity  however a huge assortment of work has been zeroing in on distinguishing feelings in discourse [28,35,65]. Looks: While the previously mentioned past work gives very great outcomes, it may not be consistently appropriate in instructive setting. Discourse is frequently not needed while speaking with instructive specialists, and approaches that require appended sensors may not be great for the student. This leaves the discovery of looks and their examination as a decent choice. Different methodologies have been proposed to identify looks.
Early works, for example, the FACS , center around facial definition, where the highlights are distinguished and encoded as an element vector that is utilized to track down a specific feeling. Late methodologies utilize dynamic forms  or other computerized strategies to identify the elements naturally. An enormous class of calculations endeavors to utilize math based methodologies, for example, facial remaking  and others distinguish notable facial elements [20,63]. Different feelings and their varieties have been considered  and ordered , and some attention on miniature articulations . Novel approaches utilize robotized highlight recognition by utilizing AI techniques for example, support vector machine [5,58], however they share a similar reasonableness to the facial locator as the previously mentioned approaches (see likewise an audit ). One of the critical parts of these methodologies is a face global positioning framework  that ought to be fit for a vigorous identification of the face and its elements even in changing light conditions and for various students . Nonetheless, existing strategies frequently require cautious adjustment, comparable lighting conditions, and the alignment may not move to different people. Such frameworks give great outcomes to head position or direction following, however they might neglect to distinguish unobtrusive changes in temperament that are significant for feeling location. Profound Learning: Recent advances in profound learning  brought profound neural organizations additionally to the field of feeling location. A few methodologies have been presented for powerful head pivot discovery , recognition of facial highlights , discourse , or even feelings . Among them, EmoNets  recognizes acted feelings from films by all the while breaking down both video and sound transfers. This methodology expands on the past work for CNN facial identification . Our work is roused by crafted by Burket et al.  who presented profound learning network called DeXpression for feeling location from recordings. Specifically, they utilize the Cohn-Kanade data set (CMU-Pittsburg AU coded information base)  furthermore, the MMI Facial Expression .Recurrent Neural Networks for Emotion Recognition in Video ,2015 In this work author present a complete system for the 2015 Emotion Recognition in the Wild (EmotiW) Challenge. We focus our presentation and experimental analysis on a hybrid CNN-RNN architecture for facial expression analysis that can outperform a previously applied CNN approach using temporal averaging for aggregation.
Deep Facial Expression Recognition: A Survey, 2018: In this paper, author provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. We then describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.
III. RESEARCH ISSUE & FUTURE SCOPE
In this section basically we talk about research gap which need to be solved, as per the all previous work there is no any researcher who solve the most important and critical factors and that are:
A. Future Objective
In this future our main is to resolve all previous existing issue and create a balanced system which will give a quality result in all parameters:
Human emotion analysis is a challenging machine learning task with a wide range of applications in human-computer interaction, e-learning, health care, advertising and gaming. Emotion analysis is particularly challenging as multiple input modalities, both visual and auditory, play an important role in understanding it. Given a video sequence with a human subject, some of the important cues which help to understand the user\'s emotion are facial expressions, movements and activities. In this paper basically we did the detailed study about the all-previous existing approaches and based on that we found multiple future scope on this area.
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