A fuzzy logic approach to reliable real-time recognition of facial emotions

Bahreini, Kiavash; Vegt, Wim Van der; Westera, Wim

doi:10.1007/s11042-019-7250-z

Cited by 33 publications

(20 citation statements)

References 66 publications

(85 reference statements)

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“…Kiavash Bahreini, Wim Van der Vegt and Wim Westera offers high quality, reliable recognition, and categorisation of emotions use by fuzzylogic based emotion recognition. This study achieves an 83.2% average accuracy which showed that most intensive emotions (e.g., happiness, surprise) can be detected better than the remainder emotions except neutral and fear [24].…”

Section: Related Workmentioning

confidence: 71%

Review on Emotion Recognition Using Facial Expressions

Atya

Bilal

2021

EJECE

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The advent of artificial intelligence technology has reduced the gap between humans and machines as equips man to create more near-perfect humanoids. Facial expression is an important tool to communicate one’s emotions as a non-verbally overview of emotion recognition using facial expressions. A remarkable advantage of such a technique recently improved public security through tracking and recognizing, thus led to the high attention to keep up the scientific research in the field. The approaches used for facial expression include classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolution Neural Network (CNN), Active Appearance and Machine learning which all used to classify emotions based on certain parts of interest on the face like lips, lower jaw, eyebrows, cheeks and many more. By comparison, the reviews have shown that the average accuracy of the basic emotion ranged from 51% up to 100%, whereas carrying through 7% to 13% in the compound emotions, hence indicated that the indispensable emotion is much comfortable to recognize.

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Section: Related Workmentioning

confidence: 71%

Review on Emotion Recognition Using Facial Expressions

Atya

Bilal

2021

EJECE

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“…Emotion-related data are also saved in the same database (heart-rate values or emotions, in the case of non-VR use of the system). For detecting emotions through the facial expressions captured by the webcam, we used the RAGE (Realising an Applied Gaming “Emotion Detection Asset” created by the Open University of the Netherlands [ 81 ] which is capable of identifying the six basic emotions: happiness, sadness, surprise, fear, disgust, and anger [ 82 ] or if the user is having a neutral expression.…”

Section: Our Systemmentioning

confidence: 99%

Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement

Stănică

Moldoveanu

Portelli

et al. 2020

Sensors

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As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system’s accuracy and investigate a wireless alternative based on neural networks.

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“…Cho et al [52] measured human stress using a respiration variability spectrogram (RVS), which was measured with a thermal imaging camera using CNN. In addition to the machine learning and deep learning mentioned above, many studies have been conducted to analyze emotions using fuzzy theory [25,53,54]. Aside from that, deep learning is used in various fields [55].…”

Section: Emotion Classification Using Machine Learning and Deep Learningmentioning

confidence: 99%

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

Lee

Kim

2020

Sensors

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This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

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A fuzzy logic approach to reliable real-time recognition of facial emotions

Cited by 33 publications

References 66 publications

Review on Emotion Recognition Using Facial Expressions

Review on Emotion Recognition Using Facial Expressions

Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

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