scholarly journals Emotion Detection using Facial Recognition Technique

2021 ◽  
pp. 413-417
Author(s):  
Ritvik Tiwari ◽  
Rudra Thorat ◽  
Vatsal Abhani ◽  
Shakti Mahapatro
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Research Field ◽  
Emotion Detection ◽  
Human Machine Interfaces ◽  
Different Types ◽  
Recognition Technique

Emotion recognition based on facial expression is an intriguing research field, which has been presented and applied in various spheres such as safety, health and in human machine interfaces. Researchers in this field are keen in developing techniques that can prove to be an aid to interpret, decode facial expressions and then extract these features in order to achieve a better prediction by the computer. With advancements in deep learning, the different types of prospects of this technique are exploited to achieve a better performance. We spotlight these contributions, the architecture and the databases used and present the progress made by comparing the proposed methods and the results obtained. The interest of this paper is to guide the technology enthusiasts by reviewing recent works and providing insights to make improvements to this field.

scholarly journals Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Edeh Michael Onyema ◽  
Piyush Kumar Shukla ◽  
Surjeet Dalal ◽  
Mayuri Neeraj Mathur ◽  
Mohammed Zakariah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Computational Cost ◽  
Machine Learning Algorithms ◽  
Expression Recognition ◽  
Deep Learning Algorithm

The use of machine learning algorithms for facial expression recognition and patient monitoring is a growing area of research interest. In this study, we present a technique for facial expression recognition based on deep learning algorithm: convolutional neural network (ConvNet). Data were collected from the FER2013 dataset that contains samples of seven universal facial expressions for training. The results show that the presented technique improves facial expression recognition accuracy without encoding several layers of CNN that lead to a computationally costly model. This study proffers solutions to the issues of high computational cost due to the implementation of facial expression recognition by providing a model close to the accuracy of the state-of-the-art model. The study concludes that deep l\earning-enabled facial expression recognition techniques enhance accuracy, better facial recognition, and interpretation of facial expressions and features that promote efficiency and prediction in the health sector.

scholarly journals On-Road Driver Emotion Recognition Using Facial Expression

2022 ◽  
Vol 12 (2) ◽  
pp. 807
Author(s):  
Huafei Xiao ◽  
Wenbo Li ◽  
Guanzhong Zeng ◽  
Yingzhang Wu ◽  
Jiyong Xue ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Data Augmentation ◽  
Recognition Task ◽  
Superior Performance ◽  
Expression Recognition ◽  
Emotion Detection ◽  
Backbone Networks ◽  
Driving Task ◽  
Machine Systems

With the development of intelligent automotive human-machine systems, driver emotion detection and recognition has become an emerging research topic. Facial expression-based emotion recognition approaches have achieved outstanding results on laboratory-controlled data. However, these studies cannot represent the environment of real driving situations. In order to address this, this paper proposes a facial expression-based on-road driver emotion recognition network called FERDERnet. This method divides the on-road driver facial expression recognition task into three modules: a face detection module that detects the driver’s face, an augmentation-based resampling module that performs data augmentation and resampling, and an emotion recognition module that adopts a deep convolutional neural network pre-trained on FER and CK+ datasets and then fine-tuned as a backbone for driver emotion recognition. This method adopts five different backbone networks as well as an ensemble method. Furthermore, to evaluate the proposed method, this paper collected an on-road driver facial expression dataset, which contains various road scenarios and the corresponding driver’s facial expression during the driving task. Experiments were performed on the on-road driver facial expression dataset that this paper collected. Based on efficiency and accuracy, the proposed FERDERnet with Xception backbone was effective in identifying on-road driver facial expressions and obtained superior performance compared to the baseline networks and some state-of-the-art networks.

scholarly journals Student Prize-Winning Abstracts 2010

2011 ◽  
Vol 12 (1) ◽  
pp. 77-77
Author(s):  
Sharpley Hsieh ◽  
Olivier Piguet ◽  
John R. Hodges
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Negative Emotions ◽  
Emotion Detection ◽  
Recognition Of Facial Expressions ◽  
Patient Groups ◽  
The Neural Networks ◽  
Emotional Dimensions ◽  
Recognition Of Emotions ◽  
Valence And Arousal

AbstractIntroduction: Frontotemporal dementia (FTD) is a progressive neurode-generative brain disease characterised clinically by abnormalities in behaviour, cognition and language. Two subgroups, behavioural-variant FTD (bvFTD) and semantic dementia (SD), also show impaired emotion recognition particularly for negative emotions. This deficit has been demonstrated using visual stimuli such as facial expressions. Whether recognition of emotions conveyed through other modalities — for example, music — is also impaired has not been investigated. Methods: Patients with bvFTD, SD and Alzheimer's disease (AD), as well as healthy age-matched controls, labeled tunes according to the emotion conveyed (happy, sad, peaceful or scary). In addition, each tune was also rated along two orthogonal emotional dimensions: valence (pleasant/unpleasant) and arousal (stimulating/relaxing). Participants also undertook a facial emotion recognition test and other cognitive tests. Integrity of basic music detection (tone, tempo) was also examined. Results: Patient groups were matched for disease severity. Overall, patients did not differ from controls with regard to basic music processing or for the recognition of facial expressions. Ratings of valence and arousal were similar across groups. In contrast, SD patients were selectively impaired at recognising music conveying negative emotions (sad and scary). Patients with bvFTD did not differ from controls. Conclusion: Recognition of emotions in music appears to be selectively affected in some FTD subgroups more than others, a disturbance of emotion detection which appears to be modality specific. This finding suggests dissociation in the neural networks necessary for the processing of emotions depending on modality.

scholarly journals Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

2021 ◽  
Vol 11 (24) ◽  
pp. 11738
Author(s):  
Thomas Teixeira ◽  
Éric Granger ◽  
Alessandro Lameiras Koerich
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Affective Computing ◽  
Spatial Information ◽  
Short Term Memory ◽  
State Of The Art ◽  
Fine Tuning

Facial expressions are one of the most powerful ways to depict specific patterns in human behavior and describe the human emotional state. However, despite the impressive advances of affective computing over the last decade, automatic video-based systems for facial expression recognition still cannot correctly handle variations in facial expression among individuals as well as cross-cultural and demographic aspects. Nevertheless, recognizing facial expressions is a difficult task, even for humans. This paper investigates the suitability of state-of-the-art deep learning architectures based on convolutional neural networks (CNNs) to deal with long video sequences captured in the wild for continuous emotion recognition. For such an aim, several 2D CNN models that were designed to model spatial information are extended to allow spatiotemporal representation learning from videos, considering a complex and multi-dimensional emotion space, where continuous values of valence and arousal must be predicted. We have developed and evaluated convolutional recurrent neural networks, combining 2D CNNs and long short term-memory units and inflated 3D CNN models, which are built by inflating the weights of a pre-trained 2D CNN model during fine-tuning, using application-specific videos. Experimental results on the challenging SEWA-DB dataset have shown that these architectures can effectively be fine-tuned to encode spatiotemporal information from successive raw pixel images and achieve state-of-the-art results on such a dataset.

scholarly journals Recognition of Emotion From Facial Expression for Autism Disorder

2019 ◽  
Vol 8 (2S3) ◽  
pp. 530-532
Keyword(s):  
Facial Expression ◽  
Interpersonal Relationships ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Automatic Recognition ◽  
Point Of View ◽  
Spatial Relationships ◽  
Expression Recognition ◽  
The Face ◽  
Autism Disorder

Facial expressions convey verbal indications that play an important role in interpersonal relationships. Despite the fact that people immediately perceive facial expressions for all intents and purposes, solid expression recognition by machine is still a challenge. From the point of view of automatic recognition, The facial expression may included the figurations of the facial parts and their spatial relationships or changes in the pigmentation of the face. The study of automatic facial recognition addresses issues relating to the static or dynamic qualities of such distortion or facial pigmentation. Use The Camera to capture the live images of autism people

scholarly journals Evaluation of Classroom Teaching Effect Based on Facial Expression Recognition

2021 ◽  
Vol 5 (12) ◽  
pp. 63-68
Author(s):  
Jun Mao
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
High Precision ◽  
Facial Expression Recognition ◽  
Facial Recognition ◽  
Classroom Teaching ◽  
Traditional Teaching ◽  
Expression Recognition

Classroom is an important environment for communication in teaching events. Therefore, both school and society should pay more attention to it. However, in the traditional teaching classroom, there is actually a relatively lack of communication and exchanges. Facial expression recognition is a branch of facial recognition technology with high precision. Even in large teaching scenes, it can capture the changes of students’ facial expressions and analyze their concentration accurately. This paper expounds the concept of this technology, and studies the evaluation of classroom teaching effects based on facial expression recognition.

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