scholarly journals WHO CAN READ YOUR FACIAL EXPRESSION? A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Sunglasses and Facemasks

2021 ◽  
Author(s):  
Harisu Abdullahi Shehu ◽  
Will N. Browne ◽  
Hedwig Eisenbarth
Keyword(s):  
Facial Expressions ◽  
Learning Algorithm ◽  
Facial Features ◽  
Emotional Facial Expressions ◽  
Lower Face ◽  
Machine Learning Classifiers ◽  
Deep Learning Algorithm ◽  
Different Types ◽  
The Impact ◽  
Better Than

Partial face coverings such as sunglasses and facemasks have now become the ‘new norm’, especially since the increase of infectious diseases. Unintentionally, they obscure facial expressions. Therefore, humans and artificial systems have been found to be less accurate in emotion categorization. However, it is unknown how similar the performance of humans compared with artificial systems is affected based on the exact same stimuli, varying systematically in types of coverings. Such a systematic direct comparison would allow conclusions about the relevant facial features in a naturalistic context. Therefore, we investigated the impact of facemasks and sunglasses on the ability to categorize emotional facial expressions in humans and artificial systems. Artificial systems, represented by the VGG19 deep learning algorithm, and humans assessed images of people with varying emotional facial expressions and with four different types of coverings, i.e. unmasked (original images), mask (mask covering lower-face), partial mask (with transparent mouth window), and sunglasses. Artificial systems performed significantly better than humans when no covering is present (> 15% difference). However, the achieved accuracy of both humans and artificial systems differed significantly depending on the type of coverings and, importantly, emotion, e.g. the use of sunglasses reduced accuracy for recognition of fear in humans. It was also noted that while humans mainly classify unknown expressions as neutral across all coverings, the misclassification varied in the artificial systems. These findings show humans and artificial systems classify and misclassify various emotion expressions differently depending on both the type of face covering and type of emotion.

scholarly journals Research on the Design of Government Affairs Platform in the Context of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qian Huang ◽  
Xue Wen Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Algorithm ◽  
The Internet ◽  
Chinese Government ◽  
Development Status ◽  
Deep Learning Algorithm ◽  
Government Management ◽  
The Government ◽  
The Impact

Big data is a massive and diverse form of unstructured data, which needs proper analysis and management. It is another great technological revolution after the Internet, the Internet of Things, and cloud computing. This paper firstly studies the related concepts and basic theories as the origin of research. Secondly, it analyzes in depth the problems and challenges faced by Chinese government management under the impact of big data. Again, we explore the opportunities that big data brings to government management in terms of management efficiency, administrative capacity, and public services and believe that governments should seize opportunities to make changes. Brainlike computing attempts to simulate the structure and information processing process of biological neural network. This paper firstly analyzes the development status of e-government at home and abroad, studies the service-oriented architecture (SOA) and web services technology, deeply studies the e-government and SOA theory, and discusses this based on the development status of e-government in a certain region. Then, the deep learning algorithm is used to construct the monitoring platform to monitor the government behavior in real time, and the deep learning algorithm is used to conduct in-depth mining to analyze the government's intention behavior.

scholarly journals Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoting Yin ◽  
Xiaosha Tao
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Process ◽  
Deep Learning Algorithm ◽  
Sales Prediction ◽  
Different Types ◽  
Online Business ◽  
Product Sales ◽  
Deep Learning Model

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users’ preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.

scholarly journals Deep learning based facial expressions recognition system for assisting visually impaired persons

2020 ◽  
Vol 9 (3) ◽  
pp. 1208-1219
Author(s):  
Hendra Kusuma ◽  
Muhammad Attamimi ◽  
Hasby Fahrudin
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Visually Impaired ◽  
Learning Algorithm ◽  
Recognition System ◽  
Verbal Information ◽  
Deep Learning Algorithm ◽  
Facial Recognition System ◽  
Visually Impaired Persons ◽  
Facial Expressions Recognition

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.

scholarly journals Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Yang Zhao ◽  
Liang He ◽  
Xiao Qi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Mechanism Model ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Stacked Autoencoders ◽  
Different Types ◽  
Operation Status ◽  
Types Of Faults

The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.

scholarly journals Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8 ◽  
Author(s):  
Zhongxiao Wang
Keyword(s):  
Deep Learning ◽  
Physical Training ◽  
Evaluation System ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Sports Training ◽  
Deep Learning Algorithm ◽  
The Impact ◽  
Physical Index

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.

scholarly journals Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xueying Li ◽  
Pingping Fan ◽  
Zongmin Li ◽  
Guangyuan Chen ◽  
Huimin Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
Different Types

Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.

scholarly journals Deep Learning for Identification of Acute Illness and Facial Cues of Illness

2021 ◽  
Vol 8 ◽  
Author(s):  
Castela Forte ◽  
Andrei Voinea ◽  
Malina Chichirau ◽  
Galiya Yeshmagambetova ◽  
Lea M. Albrecht ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
External Validation ◽  
Facial Feature ◽  
Acute Illness ◽  
Facial Features ◽  
Genetic Syndromes ◽  
Coronary Syndrome ◽  
Deep Learning Algorithm

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt.Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals.Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS).Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3–33.1% for the skin model) to 89.4% (66.9–98.7%, for the nose model). Specificity ranged from 42.1% (20.3–66.5%) for the nose model and 94.7% (73.9–99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62–0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35–100.00%) and specificity of 42.11% (20.25–66.50%).Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness.

scholarly journals The Influence of Game Character Appearance on Empathy and Immersion: Virtual Non-Robotic Versus Robotic Animals

2020 ◽  
Vol 51 (5) ◽  
pp. 685-711
Author(s):  
Alexandra Sierra Rativa ◽  
Marie Postma ◽  
Menno Van Zaanen
Keyword(s):  
Facial Expressions ◽  
User Experience ◽  
Emotional Facial Expressions ◽  
Experimental Conditions ◽  
Affective Outcomes ◽  
Visual Appearance ◽  
Simulated Environment ◽  
Body Appearance ◽  
Facial Expressiveness ◽  
The Impact

Background. Empathic interactions with animated game characters can help improve user experience, increase immersion, and achieve better affective outcomes related to the use of the game. Method. We used a 2x2 between-participant design and a control condition to analyze the impact of the visual appearance of a virtual game character on empathy and immersion. The four experimental conditions of the game character appearance were: Natural (virtual animal) with expressiveness (emotional facial expressions), natural (virtual animal) with non-expressiveness (without emotional facial expressions), artificial (virtual robotic animal) with expressiveness (emotional facial expressions), and artificial (virtual robotic animal) with non-expressiveness (without emotional facial expressions). The control condition contained a baseline amorphous game character. 100 participants between 18 to 29 years old (M=22.47) were randomly assigned to one of five experimental groups. Participants originated from several countries: Aruba (1), China (1), Colombia (3), Finland (1), France (1), Germany (1), Greece (2), Iceland (1), India (1), Iran (1), Ireland (1), Italy (3), Jamaica (1), Latvia (1), Morocco (3), Netherlands (70), Poland (1), Romania (2), Spain (1), Thailand (1), Turkey (1), United States (1), and Vietnam (1). Results. We found that congruence in appearance and facial expressions of virtual animals (artificial + non-expressive and natural + expressive) leads to higher levels of self-reported situational empathy and immersion of players in a simulated environment compared to incongruent appearance and facial expressions. Conclusions. The results of this investigation showed an interaction effect between artificial/natural body appearance and facial expressiveness of a virtual character’s appearance. The evidence from this study suggests that the appearance of the virtual animal has an important influence on user experience.

Beauty matters: reducing bounce rate by aesthetics of experience product portal page

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xueqing Wang ◽  
Yang Li ◽  
Zhao Cai ◽  
Hefu Liu
Keyword(s):  
Learning Algorithm ◽  
Original Design ◽  
Content Type ◽  
Design Elements ◽  
Deep Learning Algorithm ◽  
Image Aesthetics ◽  
Product Profile ◽  
The Impact ◽  
The Relationship

PurposeThis study aims to investigate the impact of experience product portal page aesthetics on bounce rate.Design/methodology/approachThis research collected data from an online shop selling original design furniture on Taobao.com. It employed deep learning algorithm and manual coding to operationalize image and text aesthetics.FindingsThe empirical results indicate that text aesthetics has a U-shaped relationship with bounce rate, whereas the relationship between image aesthetics and bounce rate is insignificant. Moreover, the U-shaped relationship between text aesthetics and bounce rate is weakened by image aesthetics.Originality/valueThis study addresses an important but understudied topic – the bounce rate of experience products in the context of e-commerce. Although the high bounce rate has increasingly gained attention from practitioners, there remains a scarcity of research that addresses the effect of product portal page aesthetics in the specific context of experience products. The authors theorize product portal page aesthetics as the design elements of an e-commerce website and deeply analyzed the role of product portal page aesthetics by classifying it into text aesthetics and image aesthetics. The authors’ findings provide implications for online sellers and platforms to effectively design product profile pages to reduce the bounce rate.

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