Relevant bibliographies by topics / FER2013

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'FER2013'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "FER2013"

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Bodavarapu, Pavan Nageswar Reddy, and P. V. V. S. Srinivas. "Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques." Indian Journal of Science and Technology 14, no. 12 (2021): 971–83. http://dx.doi.org/10.17485/ijst/v14i12.14.

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Background/Objectives: There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. Methods: We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution images for facial expression recognition (Anger, Sad, Disgust,
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Tiwari, Er Shesh Mani, and Er Mohd Shah Alam. "Facial Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 490–94. http://dx.doi.org/10.22214/ijraset.2023.49067.

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Abstract: Facial Emotion Recognition plays a significant role in interacting with computers which help us in various fields like medical processes, to present content on the basis of human mood, security and other fields. It is challenging because of heterogeneity in human faces, lighting, orientation, poses and noises. This paper aims to improve the accuracy of facial expression recognition. There has been much research done on the fer2013 dataset using CNN (Convolution Neural Network) and their results are quite impressive. In this work we performed CNN on the fer2013 dataset by adding image
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Pavan, Nageswar Reddy Bodavarapu, and V. V. S. Srinivas P. "Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques." Indian Journal of Science and Technology 14, no. 12 (2021): 971–83. https://doi.org/10.17485/IJST/v14i12.14.

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Abstract <strong>Background/Objectives:</strong>&nbsp;There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images.&nbsp;<strong>Methods:</strong>&nbsp;We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution image
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Lou, Yang, and Dan Li. "Facial Expression Recognition Based on the FER2013 Dataset." World Journal of Innovation and Modern Technology 7, no. 5 (2024): 70–75. http://dx.doi.org/10.53469/wjimt.2024.07(05).07.

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Facial emotions are a way to express one's thoughts and also an effective way to understand the emotions of others. Nowadays, with the rapid development of technology, computers can also recognize facial expressions through convolutional neural networks, deep learning, and other methods, and classify the results. Throughout the entire experiment, we chose FER2013 data as the training set for our model, which ultimately achieved an accuracy of around 62%. We also compared it with the SFEW dataset. The emergence of facial expression recognition will increase in the future, and its application in
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Amri, Maryama Kurnia, and Bambang Sugiantoro. "FaceGAN: Robust Face Recognition using Generative Adversarial Networks (GAN) Algorithm." International Journal of Informatics and Computation 5, no. 1 (2023): 39. http://dx.doi.org/10.35842/ijicom.v5i1.57.

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Generative Adversarial Networks (GANs) are a type of neural network that can generate synthetic images that are often indistinguishable from real ones. The article explores GAN to augment existing datasets or generate new ones for training classifiers. The competitive training process of GANs results in a generator network that can produce increasingly realistic images to create more diverse and balanced datasets for training classifiers. The article discusses several successful applications of GANs in image classification, including object recognition, face classification, and medical image a
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Bahri, Saeful, Riza Samsinar, and Panggalih Sako Denta. "Pengenalan Ekspresi Wajah untuk Identifikasi Psikologis Pengguna dengan Neural Network dan Transformasi Ten Crops." RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) 5, no. 1 (2022): 15. http://dx.doi.org/10.24853/resistor.5.1.15-20.

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Pada penelitian ini, dirancang sebuah sistem pengenalan ekspresi wajah untuk identifikasi psikologis pengguna dari data mentah tersimpan dengan model neural network yang dirancang berdasarkan AlexNet dan VGG19 dengan 1 dropout layer tanpa ReLU layer pada classifier layer untuk mengurangi jumlah kebutuhan memori yang dipakai pada GPU dan waktu proses secara signifikan sehingga dapat digunakan pada perangkat berdaya komputasi terbatas. Dataset yang digunakan adalah CK+ dataset dan FER2013 dataset dengan transformasi ten crops. Didapatkan waktu proses yang lebih singkat secara signifikan dibandin
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Viola Bakiasi (Shtino). "Real-Time Micro-Expression Recognition Using YOLOv8 and FER2013 Dataset." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 53–64. https://doi.org/10.52783/jisem.v10i18s.2883.

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Facial micro-expression recognition plays a crucial role in affective computing, human-computer interaction, and psychological analysis. This study implements a real-time face emotion detection system using the YOLOv8 (You Only Look Once version 8) model. The proposed approach leverages YOLO’s real-time processing capability and deep learning-based feature extraction to detect subtle facial muscle movements with high precision. The methodology involves pre-processing the FER2013 (Facial Expression Recognition 2013) dataset, which consists of grayscale images categorized into seven emotional cl
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Kusno, Jasen Wanardi, and Andry Chowanda. "Modeling Emotion Recognition System from Facial Images Using Convolutional Neural Networks." CommIT (Communication and Information Technology) Journal 18, no. 2 (2024): 251–59. http://dx.doi.org/10.21512/commit.v18i2.8873.

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Emotion classification is the process of identifying human emotions. Implementing technology to help people with emotional classification is considered a relatively popular research field. Until now, most of the work has been done to automate the recognition of facial cues (e.g., expressions) from several modalities (e.g., image, video, audio, and text). Deep learning architecture such as Convolutional Neural Networks (CNN) demonstrates promising results for emotion recognition. The research aims to build a CNN model while improving accuracy and performance. Two models are proposed in the rese
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Bilous, N. V., O. V. Rassokha, I. A. Ahekian, and O. V. Hramm. "Research on Methods for Development of Software System for Emotions Recognition and State of Human Health Determination." Bionics of Intelligence 1, no. 94 (2020): 65–70. http://dx.doi.org/10.30837/bi.2020.1(94).10.

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To determine the general state of human health using emotion recognition, a method based on machine learningwas chosen, the classifier is trained on the dataset “fer2013” and “PAB-F”. negative emotions and the use of a dataset totrain the neural network, with images of people divided into classes “painful” - “not painful”. For the “fer2013” dataset,it is necessary to determine the presence of pain after data processing. As a rule, pain is expressed in the intense andprolonged presence of emotions of anger and sadness. I suggest measuring the intensity of emotion by the probabilityfactor of det
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Gao, Zhiming. "Comparison of CNN and ResNet Neural Networks on the Performance of Facial Expression Recognition." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 31–38. http://dx.doi.org/10.54097/cx6rc461.

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Facial expression recognition is a crucial task in numerous applications, including human-computer interaction, mental health monitoring, and human behavior analysis. Previous studies have primarily focused on individual models or techniques for improving emotion classification accuracy. However, a comparative analysis of different neural network architectures' performance for facial expression recognition is lacking. The main objective of this study is to compare the performance of Convolutional Neural Network (CNN) and Residual Network (ResNet) on the Fer2013 dataset. The author aims to anal
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Dissertations / Theses on the topic "FER2013"

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Nordén, Frans, and Reis Marlevi Filip von. "A Comparative Analysis of Machine Learning Algorithms in Binary Facial Expression Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254259.

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In this paper an analysis is conducted regarding whether a higher classification accuracy of facial expressions are possible. The approach used is that the seven basic emotional states are combined into a binary classification problem. Five different machine learning algorithms are implemented: Support vector machines, Extreme learning Machine and three different Convolutional Neural Networks (CNN). The utilized CNN:S were one conventional, one based on VGG16 and transfer learning and one based on residual theory known as RESNET50. The experiment was conducted on two datasets, one small contai
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Book chapters on the topic "FER2013"

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Luo, Li, Jianjun He, and Huapeng Cai. "The Method for Micro Expression Recognition Based on Improved Light-Weight CNN." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_76.

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AbstractIn view of the particularity of micro expression, there are some problems, such as resource waste or parameter redundancy in micro expression training and recognition by using large convolutional neural network model alone. Therefore, a method of using lightweight model to recognize micro expression is proposed, which aims to reduce the size of model space and the number of parameters, and improve the accuracy at the same time. This method uses mini-Xception as the framework and Non-Local Net and SeNet as parallel auxiliary feature extractors to enhance feature extraction. Finally, the
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Amal, V. S., Sanjay Suresh, and G. Deepa. "Real-Time Emotion Recognition from Facial Expressions Using Convolutional Neural Network with Fer2013 Dataset." In Smart Innovation, Systems and Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3675-2_41.

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Quiroz-Martínez, Miguel-Ángel, Sergio Díaz-Fernández, Kevin Aguirre-Sánchez, and Mónica-Daniela Gómez-Ríos. "Analysis of Students’ Emotions in Real-Time During Class Sessions Through an Emotion Recognition System." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_8.

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Abstract Recognizing and responding to students’ emotional states during classes is crucial for optimizing learning outcomes, yet it remains challenging for educators. This study presents the development and implementation of a real-time emotion recognition system using convolutional neural networks (CNNs) to analyze students’ facial expressions during in-person classes. Trained on the FER2013 dataset, the system classifies seven distinct emotions with 85% accuracy. An experiment with 20 university students aged 18–25 compared emotional responses across three teaching methodologies: collective
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E. Irhebhude, Martins, Adeola O. Kolawole, and Goshit Nenbunmwa Amos. "Perspective on Dark-Skinned Emotion Recognition using Deep-Learned and Handcrafted Feature Techniques." In Emotion Recognition - Recent Advances, New Perspectives and Applications [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.109739.

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Image recognition has been widely used in various fields of applications such as human—computer interaction, where it can enhance fluency, accuracy, and naturalness in interaction. The need to automate the decision on human expression is high. This paper presents a technique for emotion recognition and classification based on a combination of deep-learned and handcrafted features. Residual Network (ResNet) and Rotation Invariant Local Binary Pattern (RILBP) features were combined and used as features for classification. The aim is to classify, identify, and make judgment on facial images from
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Kulkarni, Aniruddha Arvind, and Bhuvaneswari Amma N. G. "A Novel Facial Expression Recognition System Using Convolutional Neural Network." In Advances in IT Standards and Standardization Research. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-4759-1.ch010.

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Effective communication requires the use of facial expressions, which are the most natural response in the human heart. Clinical practices and behavioral description are important for human-computer interaction, and facial expression recognition (FER) is also important. There has been a lot of study on FER, with expanding applications such as neuromarketing and avatar animation. Machine learning techniques have a difficult time recognizing facial expressions because people can differ in how they display their emotions. Even a photograph of the same person in one expression can differ in bright
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Wu, Yue. "Facial Expression Recognition in Classroom Environment Based on Attention Mechanism." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230873.

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In view of the lack of facial expression data set in the classroom environment, the classroom expression data set was constructed, including the acquisition and preprocessing of students’ face pictures, the selection of students’ emotional categories in the classroom environment and the labeling of pictures. Based on the Resnet50 network model, a network structure with attention module is proposed, so that it can focus on the feature parts that clearly represent the target emotion in facial images, so as to enhance the accuracy of facial emotion recognition. In order to verify the effect of th
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Hu, Pengfei, Qiao Kang, Can Zeng, Fangyan Dong, and Kewei Chen. "Facial Expression Recognition Based on Improved Residual Network." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221052.

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Facial expression is the external expression of inner emotion, and is the main medium for analyzing emotion-guided sentiment, which has great research value. And for the problems of low accuracy and slow convergence of current models for face expression recognition. This paper proposed the ReSE face expression recognition model based on the residual network through the study of face expression recognition. The ReSE model is mainly constructed by ResNet18 and RE module, while using PReLU function instead of ReLU function, introducing Dropout function, and finally using island loss classificatio
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Conference papers on the topic "FER2013"

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Thomas, Bernard, Adityavikram Bhatt, and Shailendra Narayan Singh. "Recognition of Facial Emotions Using CNN Architecture and FER2013." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739309.

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Abdellaoui, Benyoussef, Aniss Moumen, Younes El Bouzekri El Idrissi, and Ahmed Remaida. "Training the Fer2013 Dataset with Keras Tuner." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010735600003101.

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Luo, Jinyu, Zhuocheng Xie, Feiyao Zhu, and Xiaohu Zhu. "Facial Expression Recognition using Machine Learning models in FER2013." In 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC). IEEE, 2021. http://dx.doi.org/10.1109/icftic54370.2021.9647334.

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Sahoo, Goutam Kumar, Jayakrishna Ponduru, Santos Kumar Das, and Poonam Singh. "Deep Leaning-Based Facial Expression Recognition in FER2013 Database: An in-Vehicle Application." In 2022 IEEE 19th India Council International Conference (INDICON). IEEE, 2022. http://dx.doi.org/10.1109/indicon56171.2022.10040121.

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Chakraborty, Akanksha, R. S. Sri Dharshini, K. Shruthi, and R. Logeshwari. "Recognition of American Sign Language with Study of Facial Expression for Emotion Analysis." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-238mcg.

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Sign Language is a medium of communication for many disabled people. This real-time Sign Language Recognition (SLR) system is developed to identify the words of American Sign Language (ASL) in English and translate them into 5 spoken languages (Mandarin, Spanish, French, Italian, and Indonesian). Combining the study of facial expression with the recognition of Sign Language is an attempt to understand the emotions of the signer. Mediapipe and LSTM with a Dense network are used to extract the features and classify the signs respectively. The FER2013 data set was used to train the Convolutional
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Moraes, Rodrigo C., Elloá B. Guedes, and Carlos Maurício S. Figueiredo. "Facial Expressions Classification with Ensembles of Convolutional Neural Networks and Smart Voting." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4448.

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Facial Expression is a very important factor in the social interaction of human beings. And technologies that can automatically interpret and respond to stimuli of facial expressions already find a wide variety of applications, from antidepressant drug testing to fatigue analysis of drivers and pilots. In this context, the following work presents a model for Automatic Classification of Facial Expression using as a training base the dataset Challenges in Representation Learning (FER2013), characterized by examples of spontaneous facial expressions in uncontrolled environments. The presented met
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Silva, João Marcos, Romuere Silva, Rodrigo Veras, Kelson Aires, and Laurindo Britto Neto. "Facial Expression Recognition to Aid Visually Impaired People." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18888.

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Facial expression recognition systems can help a visually impaired person to identify the emotions of the person with whom she interacts, assisting in her non-verbal communication. Among the various researches carried out in recent years on recognition of facial expressions, the best results obtained come from methods that use deep learning, mainly with the use of convolutional neural networks. This work presents a literature review on the problem of recognition of facial expressions, through the use of convolutional neural networks and proposes two approaches in which the first one uses pre-t
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Rathod, Kanchan Yadav, and Tanuja Pattanshetti. "YouTube Music Recommendation System Based on Face Expression." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-r8573m.

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Nowadays face recognition system is widely used in every field of computer vision applications such as Face lock-in smartphones, surveillance, smart attendance system, and driverless car technology. Because of this, the demand for face recognition systems is increasing day by day in the research field. The aim of this project is to develop a system that will recommend music based on facial expressions. The face-recognition system consists of object detection and identifying facial features from input images, and the face recognition system can be made more accurate with the use of convolutiona
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