Emotion Recognition by Facial Expression and Voice: Review and Analysis

Abstract

Emotion is a scorching topic in the recent years due to the critical unseen stress incurred during the pandemic and post-pandemic. This is worsening with the recent economy’s inflation and increase of living cost, many employees are seriously affected and drawn forth many families saddened cases and tremendous drop of working performance. The increasing stress brings a lot of harm not only to the individual but to the company’s and country’s growth. To recognize emotion through a single model is less accurate, however, recruiting multiple-models may lead to latency in data processing and possibly misleading results if the input models data are not properly filtered and segmented. This paper will review, analyze and theoretically compare 15 facial expression methods and 17 voice methods of emotion recognition research works. It will outline the pros and cons of each method and discuss the accuracy of some of the standalone and hybrid emotion recognition methods. Some of the methods (such as CNN, KNN and SVM) can span over multiple-models, but reveal different level of strengths. This is very important to discover, so that one may replace or enhance the weaker level if applying the same method across the multiple-models. This paper will also illustrate different levels of popularity of the methods in each model for visual comparison in ease. Hopefully, it can cater the new researchers a quick identification on the most suitable method for recognizing the emotion through facial expression and/or voice.

AIRA: An Intelligent Recommendation Agent Application for Movies

“…Future work in the field includes incorporating the latest content, optimizing recommendation algorithms, addressing data sparsity and dimensionality, exploring new data reduction techniques, and incorporating additional user features. The goal is to provide accurate and efficient recommendations that enhance user satisfaction and overall user experience [35].…”

“…Emotions are vital in our daily existence, specifically in safeguarding children. Several perspectives suggest it can impact individuals' performance, activity, and cognitive functioning [35]. Using recommendation systems is prevalent throughout several domains, encompassing OTT platforms, search engines, publications, music, and videos.…”

Robust Image Watermarking With Quaternion Fractional-Order Polar Harmonic-Fourier Moments Based On Wavelet Transformation: Resistance Against Rotation Attacks

“…The discrete wavelet transform of an image signal can be seen as higher and lower pass filtering of the two-dimensional signal's columns and rows, equivalent to a four-channel filter operation [16]. The two-dimensional signal can then be retrieved from subbands LL1, LH1, HL1, and HH1.…”

Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM

“…The Support Vector Machine (SVM) is a binary classification algorithm developed by Vapnik and Cortes based on small sample data [15]. It is based on statistical learning theory and structural risk minimization [16], which makes it more suitable for limited training data compared to other machine learning classifiers [17]. Dropout prediction is essentially a problem of identifying two types of data, and SVM is primarily designed to find the optimal hyperplane for two types of data and maximize the separation between them [18].…”

“…In this work, we adopt the Accuracy shown in formula ( 14), the Precision shown in formula (15), the Recall shown in formula ( 16) and the F measure − shown in formula (17) as the components of the evaluation matrix, which are the common evaluation criteria for supervised machine learning classifiers [19,25].…”