scholarly journals Emotion Recognition using Convolutional Neural Network in Virtual Meeting Environment

2021 ◽  
Vol 13 (1) ◽  
pp. 30-38
Author(s):  
Nabila Husna Shabrina ◽  
Julando Omar ◽  
Akmal Nusa Bhakti ◽  
Axel Patria
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Recognition System ◽  
Cascade Classifier ◽  
Training Process ◽  
Training Set ◽  
Test Set ◽  
Virtual Meeting ◽  
Haar Cascade

This study is done in order to propose an Emotion Recognition System that uses Convolutional Neural Network in a Virtual Meeting Environment to detect non-verbal feedback that emerge when communicating. This study starts with the training process of the CNN model with version 2.3.0 of tensorflow-gpu library, along with FER-2013 dataset, where only 80% of the data is used as the training set, and the other 20% is used as the test set. The model is trained for 430 epochs that results in 73.86% rate of accuracy with a loss of 1.42. In the classification process, a Haar-Cascade Classifier algorithm is used to detect faces within an image that has been inputted using OpenCV. Next the already developed model is used to predict the image that has been pre-processed. Based on the results shown, it can be concluded that the study has provided satisfactory results and is expected to help in understanding non-verbal input given when communicating and among other various things.

scholarly journals YORÙBÁNET: A DEEP CONVOLUTIONAL NEURAL NETWORK DESIGN FOR YORÙBÁ ALPHABETS RECOGNITION

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Oyeniran Oluwashina Akinloye ◽  
Oyebode Ebenezer Olukunle
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Design ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Test Set ◽  
Novel Technique ◽  
Optical Character

Numerous works have been proposed and implemented in computerization of various human languages, nevertheless, miniscule effort have also been made so as to put Yorùbá Handwritten Character on the map of Optical Character Recognition. This study presents a novel technique in the development of Yorùbá alphabets recognition system through the use of deep learning. The developed model was implemented on Matlab R2018a environment using the developed framework where 10,500 samples of dataset were for training and 2100 samples were used for testing. The training of the developed model was conducted using 30 Epoch, at 164 iteration per epoch while the total iteration is 4920 iterations. Also, the training period was estimated to 11296 minutes 41 seconds. The model yielded the network accuracy of 100% while the accuracy of the test set is 97.97%, with F1 score of 0.9800, Precision of 0.9803 and Recall value of 0.9797.

scholarly journals Emotion Recognition of Chinese Paintings at the Thirteenth National Exhibition of Fines Arts in China Based on Advanced Affective Computing

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Li ◽  
Dongliang Chen ◽  
Ning Yu ◽  
Ziping Zhao ◽  
Zhihan Lv
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Chinese Painting ◽  
Training Set ◽  
Chi Square ◽  
Test Set ◽  
Chi Square Test ◽  
Multimodal Emotion Recognition ◽  
Traditional Chinese Painting

Today, with the rapid development of economic level, people’s esthetic requirements are also rising, they have a deeper emotional understanding of art, and the voice of their traditional art and culture is becoming higher. The study expects to explore the performance of advanced affective computing in the recognition and analysis of emotional features of Chinese paintings at the 13th National Exhibition of Fines Arts. Aiming at the problem of “semantic gap” in the emotion recognition task of images such as traditional Chinese painting, the study selects the AlexNet algorithm based on convolutional neural network (CNN), and further improves the AlexNet algorithm. Meanwhile, the study adds chi square test to solve the problems of data redundancy and noise in various modes such as Chinese painting. Moreover, the study designs a multimodal emotion recognition model of Chinese painting based on improved AlexNet neural network and chi square test. Finally, the performance of the model is verified by simulation with Chinese painting in the 13th National Exhibition of Fines Arts as the data source. The proposed algorithm is compared with Long Short-Term Memory (LSTM), CNN, Recurrent Neural Network (RNN), AlexNet, and Deep Neural Network (DNN) algorithms from the training set and test set, respectively, The emotion recognition accuracy of the proposed algorithm reaches 92.23 and 97.11% in the training set and test set, respectively, the training time is stable at about 54.97 s, and the test time is stable at about 23.74 s. In addition, the analysis of the acceleration efficiency of each algorithm shows that the improved AlexNet algorithm is suitable for processing a large amount of brain image data, and the acceleration ratio is also higher than other algorithms. And the efficiency in the test set scenario is slightly better than that in the training set scenario. On the premise of ensuring the error, the multimodal emotion recognition model of Chinese painting can achieve high accuracy and obvious acceleration effect. More importantly, the emotion recognition and analysis effect of traditional Chinese painting is the best, which can provide an experimental basis for the digital understanding and management of emotion of quintessence.

scholarly journals Recognition of Face Emotion using Convolutional Neural Network

2020 ◽  
Vol 9 (9) ◽  
pp. 364-376
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Recognition System ◽  
Video Stream ◽  
Machine Learning Algorithms ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Recognition of face emotion has been a challenging task for many years. This work uses machine learning algorithms for both, a real-time image or a stored database image in the area of facial emotion recognition system. So it is very clear that, deep learning technology becomes important for Human-computer interaction (HCI) applications. The proposed system has two parts, real-time based facial emotion recognition system and also the image based facial emotion recognition system. A Convolutional Neural Network (CNN) model is used to train and test different facial emotion images in this research work. This work was executed successfully using Python 3.7.6 platform. The input Face image of a person was taken using the webcam video stream or from the standard database available for research. The five different facial emotions considered in this work are happy, surprise, angry, sad and neutral. The best recognition accuracy with the proposed system for the webcam video stream is found to be 91.2%, whereas for the input database images is found to be 90.08%.

scholarly journals The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries

Insects ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1134
Author(s):  
Mark T. Fowler ◽  
Rosemary S. Lees ◽  
Josias Fagbohoun ◽  
Nancy S. Matowo ◽  
Corine Ngufor ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Accuracy Score ◽  
Training Set ◽  
Egg Development ◽  
Test Set ◽  
Fertility Status ◽  
Mean Time

Pyriproxyfen (PPF) may become an alternative insecticide for areas where pyrethroid-resistant vectors are prevalent. The efficacy of PPF can be assessed through the dissection and assessment of vector ovaries. However, this reliance on expertise is subject to limitations. We show here that these limitations can be overcome using a convolutional neural network (CNN) to automate the classification of egg development and thus fertility status. Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l. An. gambiae Akron, and An. funestus s.l., whose fertility status and PPF exposure were known. Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy. This CNN model achieved an accuracy score of 94%, and application took a mean time of 38.5 s. Such a CNN can achieve an acceptable level of precision in a quick, robust format and can be distributed in a practical, accessible, and free manner. Furthermore, this approach is useful for measuring the efficacy and durability of PPF treated bednets, and it is applicable to any PPF-treated tool or similarly acting insecticide.

scholarly journals Emotion Recognition using Convolutional Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 1748-1765
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Medical Science ◽  
Recognition System ◽  
Activation Function ◽  
State Observation ◽  
Human Needs ◽  
Hidden Layer

Emotion recognition system place the important role in many fields, particularly image processing, medical science, machine learning. As per human needs, the effect and potential use of programmed emotion recognition have been developing in a wide scope of utilizations, including human-PC communication, robot control and driver state observation. In any case, to date, vigorous acknowledgment of outward appearances from pictures and recordings is yet a testing errand because of the trouble in precisely extricating the helpful passionate highlights. These highlights are regularly spoken to in various structures, for example, static, dynamic, point-based geometric or area based appearance. Facial development highlights, which incorporate component position and shape changes, are by and large brought about by the developments of facial components and muscles on the face of enthusiastic manner. Emotion recognition system has many applications. and it plays a vital part in fault detection and in gaming application. In this project the emotion recognition is of dynamic way and not like uploading the image and finding the emotion. And this is achieved with the help of the concept of machine learning called Convolutional Neural Network. This is one of the most familiar deep learning concept. The main moto of using this concept is to maintain accuracy. The CNN consists of many intermediate state which plays the important role in producing the accurate output. The layers of CNN are input layer, hidden layer and output layer. The hidden layer is used to update weight, bias and activation function. If we use the CNN methodology the unwanted parts which is un necessary for the emotion recognition will be eliminated accurately. The CNN helps to reduce our elimination task in easier way and with minimal steps.

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