Facial emotion recognition using convolutional neural networks (FERC)

Mehendale, Ninad

doi:10.1007/s42452-020-2234-1

Cited by 250 publications

(72 citation statements)

References 30 publications

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“…Future work includes reducing the model's mistakes by changing its behavior concerning explicit language, manually excluding insignificant features, and adding important ones such as function words ratio, maximal word length in a message, verb ratio, etc. In addition, it is believed that further study will benefit from feature importance information obtained by using the eli5 11 package. Moreover, we plan to expand the emotion classification by adding the emotions of contempt and disgust and differentiating between fear and surprise.…”

Section: Discussionmentioning

confidence: 99%

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Emotion Classification in Russian: Feature Engineering and Analysis

Kazyulina

Babij

Malafeev

2021

Lecture Notes in Computer Science

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In this paper, we address the issue of identifying emotions in Russian informal text messages. For this purpose, a new large dataset of text messages from the most popular Russian messaging/social networking services (Telegram, VK) was compiled semi-automatically. Emojis contained in the text messages were used to annotate the data for emotions expressed. This paper proposes an integrated approach to text-based emotion classification combining linguistic methods and machine learning. This approach relies on morphological, lexical, and stylistic features of the text. Furthermore, the level of expressiveness was considered as well. As a result, an emotion classification model demonstrating near-human performance was designed. In this paper, we also report on the importance of different linguistic features of the text messages for the task of automatic emotive analysis. Additionally, we perform error analysis and discover ways to improve the model in the future.

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Section: Discussionmentioning

confidence: 99%

“…Researchers have achieved good results on image-based emotion recognition [11]. However, classifying textual dialogues based on emotions is a relatively new research area.…”

Section: Related Workmentioning

confidence: 99%

Emotion Classification in Russian: Feature Engineering and Analysis

Kazyulina

Babij

Malafeev

2021

Lecture Notes in Computer Science

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“…Researchers have made extraordinary progress in facial expression detection recently, which has led to improvements in neuroscience and cognitive science that drive the advancement of research in the field of facial expression (e.g. Danisman et al, 2010;Ekman, 2017;Mal & Swarnalatha, 2017;Mehendale, 2020;Parr & Waller, 2006;Xie & Hu, 2018). Moreover, the development of computer vision and machine learning makes emotion identification much more accurate and accessible to the general population.…”

Section: Description Of the Emotion Recognition Algorithmmentioning

confidence: 99%

“…Moreover, the development of computer vision and machine learning makes emotion identification much more accurate and accessible to the general population. The most promising (e.g., Gudi, 2015;Chollet, 2017;Mehendale, 2020;Chollet, 2017) strategy for facial expression analysis is the use of deep convolutional neural networks (CNNs). CNNs differ from multi-layer perceptrons (MLPs) as CNNs have hidden layers, which are called convolutional layers.…”

Section: Description Of the Emotion Recognition Algorithmmentioning

confidence: 99%

Human emotion recognition in the significance assessment of property attributes

Renigier‐Biłozor

Janowski

Walacik

et al. 2021

J Hous and the Built Environ

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One of the largest problems in the real estate market analysis, which includes valuation, is determining the significance of individual property attributes that may affect value or attractiveness perception. The study attempts to assess the significance of selected attributes of real estate based on the detection and analysis of the emotions of potential investors. Human facial expression is a carrier of information that can be recorded and interpreted effectively via the use of artificial intelligence methods, machine learning and computer vision. The development of a reliable algorithm requires, in this case, the identification and investigation of factors that may affect the final solution of the problem, from behavioural aspects through technological possibilities. In the presented experiment, an approach that correlates the emotional states of buyers with the visualization of selected attributes of properties is utilized. The objective of this study is to develop an original method for assessing the significance of property attributes based on emotion recognition technology as an alternative to the commonly used methods in the real estate analysis and valuation, which are usually based on surveys. The empirical analysis enabled determination of the mainstream property attributes significance from evoked emotions intensity within the group of property clients (represented by 156 respondents). The significance ranking determined on the basis of the unconscious expressed facial emotions was verified and compared to the answers given in a form of questionnaire. The results have shown that the conscious declaration of the attribute ranking differs from the emotion detection conclusions in several cases.

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“…There are some recent reports that use deep learning algorithms to determine the walking problem or walking pattern. Deep learning can also be used for classification [18,19,20].…”

Section: Literature Reviewmentioning

confidence: 99%

Walking pattern analysis using deep learning for energy harvesting smart shoes with IoT

Mehendale

Shah

Kamdar

et al. 2020

Preprint

Self Cite

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Wearable Health Devices (WHDs) benefit people to monitor their health status and have become a necessity in today's world. The smart shoe is the type of WHD, that provides comfort, convenience, and fitness tracking. Hence smart shoes can be considered as one of the most useful innovations in the field of wearable devices. In this paper, we propose a unique system, in which the smart shoes are capable of energy harvesting when the user is walking, running, dancing, or carrying out any other similar activities. This generated power can be used to charge portable devices (like mobile) and to light up the LED torch. It also has Wi-Fi-that allows it to get connected to smartphones or any device on a cloud. The recorded data was used to determine the walking pattern of the user (gait analysis) using deep learning. The overall classification accuracy obtained with proposed smart shoes could reach up to 96.2 %. This gait analysis can be further used for detecting any injury or disorder that the shoe user is suffering from. One more unique feature of the proposed smart shoe is its capability of adjusting the size by using inflatable technology as per the user's comfort.

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Facial emotion recognition using convolutional neural networks (FERC)

Cited by 250 publications

References 30 publications

Emotion Classification in Russian: Feature Engineering and Analysis

Emotion Classification in Russian: Feature Engineering and Analysis

Human emotion recognition in the significance assessment of property attributes

Walking pattern analysis using deep learning for energy harvesting smart shoes with IoT

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