Facial Expression Recognition – A Comprehensive Review

Owusu, Ebenezer; Gavua, Ebenezer Komla; Zhan, Yongzhao

doi:10.47127/ijtmr.v1i4.36

Cited by 3 publications

(5 citation statements)

References 53 publications

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“…For exhaustive surveys on the past approaches on FEA and affect recognition in non-occluded faces, as well as face recognition or human detection under occlusion, readers are referred to the following work:  Introduction of early FEA approaches [Pantic and Rothkrantz 2000], [Fasel and Luettin 2003].  Surveys of recent 2D/3D FEA methods [Bettadapura 2012], [Sandbach et al 2012], [Owusu, et al 2015], [Sariyanidi, et al 2015], [Corneanu, et al 2016].…”

Section: Automatic Facial Expression Analysis Approachesmentioning

confidence: 99%

Facial Expression Analysis under Partial Occlusion

et al. 2018

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Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.

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Section: Automatic Facial Expression Analysis Approachesmentioning

confidence: 99%

Facial Expression Analysis under Partial Occlusion

et al. 2018

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“…Currently, facial expression recognition is a challenging and interesting task, as evidenced by numerous previous competitions by researchers [1][2][3][4][5][6][7][8], a group of available datasets [1,[9][10][11][12][13], and research on the subject. Many publications [14][15][16][17] have described the progress made in computer vision in recognizing emotions with faces. The extensive paper by Shan Li [15] is of particular importance because it provides a thorough explanation and review of existing and commonly used data sets for facial emotion recognition, as well as state-of-the-art (SOTA) and their respective results.…”

Section: Introductionmentioning

confidence: 99%

“…Facial Emotion Recognition 2013 (FER-2013) [1], Static Facial Emotion in the Wild (SFEW) [9], Cohn-Kanade (CK) [10], Extended Cohn-Kanade (CK+) [11], Japanese Association of Female Facial Expression (JAFFE) [12] and Expression in the Wild (ExpW) [13] are among the many sets of facial emotion recognition datasets available [14][15][16][17]. These datasets differ in many ways, which are generally described by one or more of the following factors: the amount of data, the number of emotion classes, image-based or sequential, and in conditions such as laboratory or in the wild.…”

Section: Introductionmentioning

confidence: 99%

Facial Emotion Recognition Using Convolutional Neural Network Based on the Visual Geometry Group-19

Prabaswera,

Soeparno

2023

JurnalTAM

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Facial recognition is one of many popular and difficult tasks in computer vision. A variety of research have been conducted on this subject, each of which suggests a stand-alone approach. While many studies strive for more accuracy, this study research aims to increase the efficiency of human computers by classifying emotions based on human faces using a self-based neural network. The usage of a Convolutional Neural Network (CNN) based on the Visual Geometry Group - 19 (VGG-19) classification model, which has been employed in ImageNet data sets and improved for emotion classification, is proposed in this paper. The classification process was conducted using FER-2013 dataset, which consists of over 35,000 facial images captured in various settings and contains 7 different emotions. The dataset was divided into three subsets, with 80% allocated for training, 10% for validation, and 10% for testing. With an accuracy of 71.80%, the proposed technique surpasses most self-based models.

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“…Recognizing facial emotions is a challenging and interesting task, proven with numerous previously held competitions [1][2][3][4][5][6][7][8], available datasets [1,[9][10][11][12][13][14], and conducted researches on the subject. The progresses made for computer vision in recognizing emotion by face have been described in many publications [15][16][17][18][19]. A notable mention is the extensive paper by Li and Deng [19] which gives explanatory and thorough review about existing and commonly used datasets for facial emotion recognition, along with state-of-the-art (SOTA) approaches and their respective results.…”

Section: Introductionmentioning

confidence: 99%

“…The reviews from [15][16][17][18][19] described many available facial emotion recognition datasets, such as Facial Emotion Recognition 2013 (FER-2013) [1,9], Static Facial Emotion in the Wild (SFEW) [10], Japanese Association of Female Facial Expression (JAFFE) [11], Cohn-Kanade (CK) [12], Extended Cohn-Kanade (CK+) [13], and Expression in the Wild (ExpW) [14] among many others. These datasets vary in many aspects, commonly described by one or combination of the following: amount of data, number of emotion classes, image-or sequential-based, and in lab-like or inthe-wild condition.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16

Kusuma¹,

Jonathan²,

Lim³

2020

Adv. sci. technol. eng. syst. j.

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Facial emotion recognition is one among many popular and challenging tasks in the field of computer vision. Numerous researches have been conducted on this task and each proposed either standalone-or ensemble-based processing technique. While many researches strive for better accuracy, this research also attempts to increase the processing efficiency of computer correctly classifying human emotions based on human face by utilizing a single standalone-based neural network. This research proposes the use of standalone-based modified Convolutional Neural Network (CNN) based on Visual Geometry Group-16 (VGG-16) classification model which was pretrained on ImageNet dataset and fine-tuned for emotion classification. The classification is performed on the publicly available FER-2013 dataset of over 35,000 face images with in-the-wild setting for 7 distinct emotions with the provided 80% training, 10% validation, and 10% testing data distributions. The proposed approach outperforms most standalone-based model results with 69.40% accuracy.

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Facial Expression Recognition – A Comprehensive Review

Cited by 3 publications

References 53 publications

Facial Expression Analysis under Partial Occlusion

Facial Expression Analysis under Partial Occlusion

Facial Emotion Recognition Using Convolutional Neural Network Based on the Visual Geometry Group-19

Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16

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