With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system.

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Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Student attendance is essential in the learning process. To record student attendance, several ways can be done; one of them is through student signatures. The process has several shortcomings, such as requiring a long time to make attendance; the attendance paper is lost, the administration must enter attendance data one by one into the computer. To overcome this, the paper proposed a web-based student attendance system that uses face recognition. In the proposed system, Convolutional Neural Network (CNN) is used to detect faces in images, deep metric learning is used to produce facial embedding, and K-NN is used to classify student's faces. Thus, the computer can recognize faces. From the experiments conducted, the system was able to recognize the faces of students who did attend and their attendance data was automatically saved. Thus, the university administration is alleviated in recording attendance data.

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Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

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Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

Face detection is not only one of the most studied topics in the computer vision field but also a very important task in many applications, such as security access control systems, video surveillance, human-computer interface, and image database management. Nowadays, various methods were developed for face detection systems like Viola-Jones, RCNN, SSD, and so on. Many researchers are still trying to improve face detection systems with various illustrations, poses, skin colors, and real-time detection. This paper intends to combine YOLO (You Only Look Once) algorithm with the VGG16 pre-trained convolutional neural network to propose an improvement for face detection systems. Experimental results show that proposed method has detected the test image set with over 95 % of average precision. Also, our proposed method considerably increased face detection speed in real-time live video. The experiment of this work was using the Image Processing Toolbox and the Deep Learning Toolbox in MATLAB.

Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network

Zika and Dengue are viral diseases transmitted by infected mosquitoes (Aedes aegypti) found in warm, humid environments. Mining data from social networks helps to find locations with highest density of reported cases. Differently from approaches that process text from social networks, we present a new strategy that analyzes Instagram images. We use two customized Deep Neural Networks. The first detects objects commonly used for mosquito reproduction with 85% precision. The second differentiates mosquitoes as Culex or Aedes aegypti with 82.5% accuracy. Results indicate that both networks can improve the effectiveness of current social network mining strategies such as the VazaZika project.

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Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus

This paper provides a brief insight of some famous and particularly important algorithms used for face detection. Face detection is a technology used by/ computer systems to detect faces in a given digital image. Automatic face detection is a very complex problem in image processing and many methods and algorithms have been proposed like Viola Jones, CNN (cascade neural networks), Eigenface etc. We have also tried to talk on each algorithm's efficiency and feasibility.

Nirmit Shrivastava

Papers

A survey of face detection algorithms