Facial expression recognition with polynomial Legendre and partial connection MLP

Karimi, Gholamreza; Heidarian, Mehdi

doi:10.1016/j.neucom.2020.12.070

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…Regarding image-based classification, [75] used instances from different databases to perform static image recognition of Big Six emotions, with neutral images from externally used databases. [76] presented a partially connected multilayer perceptron neural network with images from different databases. For preprocessing, images were normalized to specific pixel amounts based on different criteria [77].…”

Section: Resultsmentioning

confidence: 99%

Photogram Classification-Based Emotion Recognition

Lopez-Gil

Garay-Vitoria²

2021

IEEE Access

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This paper presents a method for facial emotion recognition based on parameterized photograms and machine learning techniques. Videos of people displaying emotions are parameterized by means of a facial feature-based emotional category association process to determine whether a given photogram expresses emotions by comparing the facial action units displayed with findings in the literature about facial emotion. To test the proposed approach, two strategies are adopted. First, photograms displaying emotions are gathered, and then different machine learning classifiers are applied to check the goodness of the obtained set of categorized emotional photograms. Second, classifiers trained on the sets of emotional photograms were then used to emotionally classify all the videos in each database, using all the photograms with no preprocessing or photogram selection. The presented method was tested using the OpenFace parameterizer with emotional videos gathered from Multimedia Understanding Facial Expression (MUG) and Cohn-Kanade (CK+) databases. The outcomes achieved for emotional photogram classification on the sets of emotional photograms reached maximums of 99.80% and 99.63% in the MUG and CK+ databases, respectively. The videos were classified using different voting strategies regarding the outcome of each photogram in the video with all the photogram emotion recognition classifiers obtained results reflecting recognition rates of 70.71% and 66.36% for the videos in MUG and CK+ databases, and reached up to 72.55% and 88.37% when classifier permutation strategies were used. The work carried out opens the door to followup work concerning data preprocessing and the use of different classifier combination methods in facial emotion recognition.

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Section: Resultsmentioning

confidence: 99%

Photogram Classification-Based Emotion Recognition

Lopez-Gil

Garay-Vitoria²

2021

IEEE Access

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“…Or neutral expression frames at the beginning and then a different number of frames near the emotional peak of the sequence [10][11][12]. As it happens with images, in most cases, neutral is not included when detecting emotions in video recordings [13,46].…”

Section: Discussionmentioning

confidence: 99%

“…Although most of these systems aim to recognize only a small number of prototypical emotional expressions, much progress has been made in developing computer systems that analyze this sort of human communication [6][7][8]. Many recent e orts on facial emotion recognition based on facial expressions have used deep learning to solve the problem, whether in static photos or dynamic video recordings [9][10][11][12][13]. Emotions in FER are becoming growingly important in many elds, such as health [14].…”

Section: Introductionmentioning

confidence: 99%

Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression

López-Gil

Gil

Garcı́a

2022

Computational Intelligence and Neuroscience

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Recent technological advancements in Artificial Intelligence make it easy to create deepfakes and hyper-realistic videos, in which images and video clips are processed to create fake videos that appear authentic. Many of them are based on swapping faces without the consent of the person whose appearance and voice are used. As emotions are inherent in human communication, studying how deepfakes transfer emotional expressions from original to fakes is relevant. In this work, we conduct an in-depth study on facial emotional expression in deepfakes using a well-known face swap-based deepfake database. Firstly, we extracted the photograms from their videos. Then, we analyzed the emotional expression in the original and faked versions of video recordings for all performers in the database. Results show that emotional expressions are not adequately transferred between original recordings and the deepfakes created from them. High variability in emotions and performers detected between original and fake recordings indicates that performer emotion expressiveness should be considered for better deepfake generation or detection.

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“…MLP neural network and composite models are normally used in industries to solve some complex problems like image recognition, radiation prediction and scene classification [34][35][36].…”

Section: Mlpmentioning

confidence: 99%

Study on design optimization of GFRP tubular column composite structure based on machine learning method

Shu,

Xue,

Zhang

et al. 2024

PLoS ONE

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Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

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Facial expression recognition with polynomial Legendre and partial connection MLP

Cited by 8 publications

References 16 publications

Photogram Classification-Based Emotion Recognition

Photogram Classification-Based Emotion Recognition

Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression

Study on design optimization of GFRP tubular column composite structure based on machine learning method

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